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Object-Level Remote Sensing Image Information Extraction and Applications
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Object-Level Remote Sensing Image Information Extraction and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 79582

Special Issue Editors

Prof. Dr. Chunhui Zhao

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Guest Editor
College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; intelligent information processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiuping Jia

E-Mail Website
Guest Editor
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
Interests: photogrammetry and remote sensing; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Li

grade E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: deep learning; artificial intelligence; feature extraction; geophysical image processing; image segmentation; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Shou Feng

E-Mail Website
Guest Editor
College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Nan Su

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Guest Editor
College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: multimodal object detection and tracking
Special Issues, Collections and Topics in MDPI journals
Dr. Yiming Yan

E-Mail Website
Guest Editor
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001,China
Interests: multi-modal information extraction; image retrieval; 3D modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technology is an important technical means for human beings to perceive the world, and multimodal remote sensing technology has become the mainstream of current research. With the rapid development of artificial intelligence technology, many new remote sensing image processing methods have been proposed. Moreover, rapid advances in remote sensing methods have also promoted the application of associated algorithms and techniques to problems in many related fields, such as classification, segmentation and clustering, target detection, etc. This Special Issue aims to report and cover the latest advances and trends concerning multimodal remote sensing image processing methods and applications. Papers of both theoretical methods and applicative techniques, as well as contributions regarding new advanced methodologies to relevant scenarios of remote sensing data, are welcome. We look forward to receiving your contributions.

Prof. Dr. Chunhui Zhao
Prof. Dr. Xiuping Jia
Prof. Dr. Wei Li
Dr. Shou Feng
Dr. Nan Su
Dr. Yiming Yan
Guest Editors

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Keywords

  • remote sensing
  • machine learning and deep learning for remote sensing
  • multispectral/hyperspectral image processing
  • LiDAR
  • SAR
  • target detection and anomaly detection
  • semantic segmentation and classification
  • object re-identification using cross-domain/cross-dimensional images
  • object 3D modeling and mesh optimization
  • applications in remote sensing

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Published Papers (21 papers)

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Research

15 pages, 3478 KiB  
Article
Improving RGB-Infrared Object Detection by Reducing Cross-Modality Redundancy
by Qingwang Wang, Yongke Chi, Tao Shen, Jian Song, Zifeng Zhang and Yan Zhu
Remote Sens. 2022, 14(9), 2020; https://doi.org/10.3390/rs14092020 - 22 Apr 2022
Cited by 41 | Viewed by 5374
Abstract
In the field of remote sensing image applications, RGB and infrared image object detection is an important technology. The object detection performance can be improved and the robustness of the algorithm will be enhanced by making full use of their complementary information. Existing [...] Read more.
In the field of remote sensing image applications, RGB and infrared image object detection is an important technology. The object detection performance can be improved and the robustness of the algorithm will be enhanced by making full use of their complementary information. Existing RGB-infrared detection methods do not explicitly encourage RGB and infrared images to achieve effective multimodal learning. We find that when fusing RGB and infrared images, cross-modal redundant information weakens the degree of complementary information fusion. Inspired by this observation, we propose a redundant information suppression network (RISNet) which suppresses cross-modal redundant information and facilitates the fusion of RGB-Infrared complementary information. Specifically, we design a novel mutual information minimization module to reduce the redundancy between RGB appearance features and infrared radiation features, which enables the network to take full advantage of the complementary advantages of multimodality and improve the object detection performance. In addition, in view of the drawbacks of the current artificial classification of lighting conditions, such as the subjectivity of artificial classification and the lack of comprehensiveness (divided into day and night only), we propose a method based on histogram statistics to classify lighting conditions in more detail. Experimental results on two public RGB-infrared object detection datasets demonstrate the superiorities of our proposed method over the state-of-the-art approaches, especially under challenging conditions such as poor illumination, complex background, and low contrast. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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27 pages, 5007 KiB  
Article
Self-Supervised Stereo Matching Method Based on SRWP and PCAM for Urban Satellite Images
by Wen Chen, Hao Chen and Shuting Yang
Remote Sens. 2022, 14(7), 1636; https://doi.org/10.3390/rs14071636 - 29 Mar 2022
Cited by 2 | Viewed by 2206
Abstract
In this paper, we propose a self-supervised stereo matching method based on superpixel random walk pre-matching (SRWP) and parallax-channel attention mechanism (PCAM). Our method is divided into two stages, training and testing. First, in the training stage, we obtain pre-matching results of stereo [...] Read more.
In this paper, we propose a self-supervised stereo matching method based on superpixel random walk pre-matching (SRWP) and parallax-channel attention mechanism (PCAM). Our method is divided into two stages, training and testing. First, in the training stage, we obtain pre-matching results of stereo images based on superpixel random walk, and some matching points with high confidence are selected as labeled samples. Then, a stereo matching network is constructed to describe the matching correlation by calculating the attention scores of any two points between different images through the parallax-channel attention mechanism, superimposing the scores of each layer to calculate the disparity. The network is trained using the labeled samples and some unsupervised constraint criteria. Finally, in the testing stage, the trained network is used to obtain stereo matching relations of stereo images. The proposed method does not need manually labeled training samples and is more suitable for 3D reconstruction under mass satellite remote sensing data. Comparative experiments on multiple datasets show that our method has a stereo matching EPE of 2.44 and a 3D reconstruction RMSE of 2.36 m. Especially in the weak texture and parallax abrupt change regions, we can achieve more advanced performance than other methods. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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25 pages, 62464 KiB  
Article
An Adaptively Attention-Driven Cascade Part-Based Graph Embedding Framework for UAV Object Re-Identification
by Bo Shen, Rui Zhang and Hao Chen
Remote Sens. 2022, 14(6), 1436; https://doi.org/10.3390/rs14061436 - 16 Mar 2022
Cited by 2 | Viewed by 2425
Abstract
With the rapid development of unmanned aerial vehicles (UAVs), object re-identification (Re-ID) based on the UAV platforms has attracted increasing attention, and several excellent achievements have been shown in the traditional scenarios. However, object Re-ID in aerial imagery acquired from the UAVs is [...] Read more.
With the rapid development of unmanned aerial vehicles (UAVs), object re-identification (Re-ID) based on the UAV platforms has attracted increasing attention, and several excellent achievements have been shown in the traditional scenarios. However, object Re-ID in aerial imagery acquired from the UAVs is still a challenging task, which is mainly due to the reason that variable locations and diverse viewpoints in UAVs platform are always resulting in more appearance ambiguities among the intra-objects and inter-objects. To address the above issues, in this paper, we proposed an adaptively attention-driven cascade part-based graph embedding framework (AAD-CPGE) for UAV object Re-ID. The AAD-CPGE aims to optimally fuse node features and their topological characteristics on the multi-scale structured graphs of parts-based objects, and then adaptively learn the most correlated information for improving the object Re-ID performance. Specifically, we first executed GCNs on the parts-based cascade node feature graphs and topological feature graphs for acquiring multi-scale structured-graph feature representations. After that, we designed a self-attention-based module for adaptive node and topological features fusion on the constructed hierarchical parts-based graphs. Finally, these learning hybrid graph-structured features with the most correlation discriminative capability were applied for object Re-ID. Several experimental verifications on three widely used UAVs-based benchmark datasets were carried out, and comparison with some state-of-the-art object Re-ID approaches validated the effectiveness and benefits of our proposed AAD-CPGE Re-ID framework. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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37 pages, 69533 KiB  
Article
A Block Shuffle Network with Superpixel Optimization for Landsat Image Semantic Segmentation
by Xuan Yang, Zhengchao Chen, Bing Zhang, Baipeng Li, Yongqing Bai and Pan Chen
Remote Sens. 2022, 14(6), 1432; https://doi.org/10.3390/rs14061432 - 16 Mar 2022
Cited by 3 | Viewed by 2760
Abstract
In recent years, with the development of deep learning in remotely sensed big data, semantic segmentation has been widely used in large-scale landcover classification. Landsat imagery has the advantages of wide coverage, easy acquisition, and good quality. However, there are two significant challenges [...] Read more.
In recent years, with the development of deep learning in remotely sensed big data, semantic segmentation has been widely used in large-scale landcover classification. Landsat imagery has the advantages of wide coverage, easy acquisition, and good quality. However, there are two significant challenges for the semantic segmentation of mid-resolution remote sensing images: the insufficient feature extraction capability of deep convolutional neural network (DCNN); low edge contour accuracy. In this paper, we propose a block shuffle module to enhance the feature extraction capability of DCNN, a differentiable superpixel branch to optimize the feature of small objects and the accuracy of edge contours, and a self-boosting method to fuse semantic information and edge contour information to further optimize the fine-grained edge contour. We label three sets of Landsat landcover classification datasets, and achieved an overall accuracy of 86.3%, 83.2%, and 73.4% on the three datasets, respectively. Compared with other mainstream semantic segmentation networks, our proposed block shuffle network achieves state-of-the-art performance, and has good generalization ability. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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19 pages, 2936 KiB  
Article
Hyperspectral Anomaly Detection Based on Improved RPCA with Non-Convex Regularization
by Wei Yao, Lu Li, Hongyu Ni, Wei Li and Ran Tao
Remote Sens. 2022, 14(6), 1343; https://doi.org/10.3390/rs14061343 - 10 Mar 2022
Cited by 20 | Viewed by 3527
Abstract
The low-rank and sparse decomposition model has been favored by the majority of hyperspectral image anomaly detection personnel, especially the robust principal component analysis(RPCA) model, over recent years. However, in the RPCA model, ℓ0 operator minimization is an NP-hard problem, which is [...] Read more.
The low-rank and sparse decomposition model has been favored by the majority of hyperspectral image anomaly detection personnel, especially the robust principal component analysis(RPCA) model, over recent years. However, in the RPCA model, ℓ0 operator minimization is an NP-hard problem, which is applicable in both low-rank and sparse items. A general approach is to relax the ℓ0 operator to ℓ1-norm in the traditional RPCA model, so as to approximately transform it to the convex optimization field. However, the solution obtained by convex optimization approximation often brings the problem of excessive punishment and inaccuracy. On this basis, we propose a non-convex regularized approximation model based on low-rank and sparse matrix decomposition (LRSNCR), which is closer to the original problem than RPCA. The WNNM and Capped ℓ2,1-norm are used to replace the low-rank item and sparse item of the matrix, respectively. Based on the proposed model, an effective optimization algorithm is then given. Finally, the experimental results on four real hyperspectral image datasets show that the proposed LRSNCR has better detection performance. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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19 pages, 3186 KiB  
Article
Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation
by Yunhao Gao, Xiukai Song, Wei Li, Jianbu Wang, Jianlong He, Xiangyang Jiang and Yinyin Feng
Remote Sens. 2022, 14(4), 850; https://doi.org/10.3390/rs14040850 - 11 Feb 2022
Cited by 21 | Viewed by 3194
Abstract
The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the [...] Read more.
The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) are fused by the CNN-based method to obtain the fused image with high spatial-spectral resolution. Secondly, considering the sequentiality of spatial distribution and spectral response, the spatial-spectral vision transformer (SSViT) is designed to extract sequential relationships from the fused images. After that, an external attention module is utilized for feature integration, and then the pixel-wise prediction is achieved for land-cover mapping. Finally, land-cover mapping and benthos data at the sites are analyzed consistently to reveal the distribution rule of benthos. Experiments on ZiYuan1-02D data of the Yellow River estuary wetland are conducted to demonstrate the effectiveness of the proposed framework compared with several related methods. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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23 pages, 10089 KiB  
Article
Multiple Superpixel Graphs Learning Based on Adaptive Multiscale Segmentation for Hyperspectral Image Classification
by Chunhui Zhao, Boao Qin, Shou Feng and Wenxiang Zhu
Remote Sens. 2022, 14(3), 681; https://doi.org/10.3390/rs14030681 - 31 Jan 2022
Cited by 12 | Viewed by 3421
Abstract
Hyperspectral image classification (HSIC) methods usually require more training samples for better classification performance. However, a large number of labeled samples are difficult to obtain because it is cost- and time-consuming to label an HSI in a pixel-wise way. Therefore, how to overcome [...] Read more.
Hyperspectral image classification (HSIC) methods usually require more training samples for better classification performance. However, a large number of labeled samples are difficult to obtain because it is cost- and time-consuming to label an HSI in a pixel-wise way. Therefore, how to overcome the problem of insufficient accuracy and stability under the condition of small labeled training sample size (SLTSS) is still a challenge for HSIC. In this paper, we proposed a novel multiple superpixel graphs learning method based on adaptive multiscale segmentation (MSGLAMS) for HSI classification to address this problem. First, the multiscale-superpixel-based framework can reduce the adverse effect of improper selection of a superpixel segmentation scale on the classification accuracy while saving the cost to manually seek a suitable segmentation scale. To make full use of the superpixel-level spatial information of different segmentation scales, a novel two-steps multiscale selection strategy is designed to adaptively select a group of complementary scales (multiscale). To fix the bias and instability of a single model, multiple superpixel-based graphical models obatined by constructing superpixel contracted graph of fusion scales are developed to jointly predict the final results via a pixel-level fusion strategy. Experimental results show that the proposed MSGLAMS has better performance when compared with other state-of-the-art algorithms. Specifically, its overall accuracy achieves 94.312%, 99.217%, 98.373% and 92.693% on Indian Pines, Salinas and University of Pavia, and the more challenging dataset Houston2013, respectively. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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28 pages, 26041 KiB  
Article
Remote Sensing Scene Image Classification Based on Self-Compensating Convolution Neural Network
by Cuiping Shi, Xinlei Zhang, Jingwei Sun and Liguo Wang
Remote Sens. 2022, 14(3), 545; https://doi.org/10.3390/rs14030545 - 24 Jan 2022
Cited by 30 | Viewed by 3844
Abstract
In recent years, convolution neural networks (CNNs) have been widely used in the field of remote sensing scene image classification. However, CNN models with good classification performance tend to have high complexity, and CNN models with low complexity are difficult to obtain high [...] Read more.
In recent years, convolution neural networks (CNNs) have been widely used in the field of remote sensing scene image classification. However, CNN models with good classification performance tend to have high complexity, and CNN models with low complexity are difficult to obtain high classification accuracy. These models hardly achieve a good trade-off between classification accuracy and model complexity. To solve this problem, we made the following three improvements and proposed a lightweight modular network model. First, we proposed a lightweight self-compensated convolution (SCC). Although traditional convolution can effectively extract features from the input feature map, when there are a large number of filters (such as 512 or 1024 common filters), this process takes a long time. To speed up the network without increasing the computational load, we proposed a self-compensated convolution. The core idea of this convolution is to perform traditional convolution by reducing the number of filters, and then compensate the convoluted channels by input features. It incorporates shallow features into the deep and complex features, which helps to improve the speed and classification accuracy of the model. In addition, we proposed a self-compensating bottleneck module (SCBM) based on the self-compensating convolution. The wider channel shortcut in this module facilitates more shallow information to be transferred to the deeper layer and improves the feature extraction ability of the model. Finally, we used the proposed self-compensation bottleneck module to construct a lightweight and modular self-compensation convolution neural network (SCCNN) for remote sensing scene image classification. The network is built by reusing bottleneck modules with the same structure. A lot of experiments were carried out on six open and challenging remote sensing image scene datasets. The experimental results show that the classification performance of the proposed method is superior to some of the state-of-the-art classification methods with less parameters. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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20 pages, 7359 KiB  
Article
SII-Net: Spatial Information Integration Network for Small Target Detection in SAR Images
by Nan Su, Jiayue He, Yiming Yan, Chunhui Zhao and Xiangwei Xing
Remote Sens. 2022, 14(3), 442; https://doi.org/10.3390/rs14030442 - 18 Jan 2022
Cited by 24 | Viewed by 3580
Abstract
Ship detection based on synthetic aperture radar (SAR) images has made a breakthrough in recent years. However, small ships, which may be regarded as speckle noise, pose enormous challenges to the accurate detection of SAR images. In order to enhance the detection performance [...] Read more.
Ship detection based on synthetic aperture radar (SAR) images has made a breakthrough in recent years. However, small ships, which may be regarded as speckle noise, pose enormous challenges to the accurate detection of SAR images. In order to enhance the detection performance of small ships in SAR images, a novel detection method named a spatial information integration network (SII-Net) is proposed in this paper. First, a channel-location attention mechanism (CLAM) module which extracts position information along with two spatial directions is proposed to enhance the detection ability of the backbone network. Second, a high-level features enhancement module (HLEM) is customized to reduce the loss of small target location information in high-level features via using multiple pooling layers. Third, in the feature fusion stage, a refined branch is presented to distinguish the location information between the target and the surrounding region by highlighting the feature representation of the target. The public datasets LS-SSDD-v1.0, SSDD and SAR-Ship-Dataset are used to conduct ship detection tests. Extensive experiments show that the SII-Net outperforms state-of-the-art small target detectors and achieves the highest detection accuracy, especially when the target size is less than 30 pixels by 30 pixels. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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28 pages, 5372 KiB  
Article
MCMS-STM: An Extension of Support Tensor Machine for Multiclass Multiscale Object Recognition in Remote Sensing Images
by Tong Gao, Hao Chen and Wen Chen
Remote Sens. 2022, 14(1), 196; https://doi.org/10.3390/rs14010196 - 2 Jan 2022
Cited by 3 | Viewed by 2010
Abstract
The support tensor machine (STM) extended from support vector machine (SVM) can maintain the inherent information of remote sensing image (RSI) represented as tensor and obtain effective recognition results using a few training samples. However, the conventional STM is binary and fails to [...] Read more.
The support tensor machine (STM) extended from support vector machine (SVM) can maintain the inherent information of remote sensing image (RSI) represented as tensor and obtain effective recognition results using a few training samples. However, the conventional STM is binary and fails to handle multiclass classification directly. In addition, the existing STMs cannot process objects with different sizes represented as multiscale tensors and have to resize object slices to a fixed size, causing excessive background interferences or loss of object’s scale information. Therefore, the multiclass multiscale support tensor machine (MCMS-STM) is proposed to recognize effectively multiclass objects with different sizes in RSIs. To achieve multiclass classification, by embedding one-versus-rest and one-versus-one mechanisms, multiple hyperplanes described by rank-R tensors are built simultaneously instead of single hyperplane described by rank-1 tensor in STM to separate input with different classes. To handle multiscale objects, multiple slices of different sizes are extracted to cover the object with an unknown class and expressed as multiscale tensors. Then, M-dimensional hyperplanes are established to project the input of multiscale tensors into class space. To ensure an efficient training of MCMS-STM, a decomposition algorithm is presented to break the complex dual problem of MCMS-STM into a series of analytic sub-optimizations. Using publicly available RSIs, the experimental results demonstrate that the MCMS-STM achieves 89.5% and 91.4% accuracy for classifying airplanes and ships with different classes and sizes, which outperforms typical SVM and STM methods. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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20 pages, 9459 KiB  
Article
Low Contrast Infrared Target Detection Method Based on Residual Thermal Backbone Network and Weighting Loss Function
by Chunhui Zhao, Jinpeng Wang, Nan Su, Yiming Yan and Xiangwei Xing
Remote Sens. 2022, 14(1), 177; https://doi.org/10.3390/rs14010177 - 1 Jan 2022
Cited by 14 | Viewed by 2949
Abstract
Infrared (IR) target detection is an important technology in the field of remote sensing image application. The methods for IR image target detection are affected by many characteristics, such as poor texture information and low contrast. These characteristics bring great challenges to infrared [...] Read more.
Infrared (IR) target detection is an important technology in the field of remote sensing image application. The methods for IR image target detection are affected by many characteristics, such as poor texture information and low contrast. These characteristics bring great challenges to infrared target detection. To address the above problem, we propose a novel target detection method for IR images target detection in this paper. Our method is improved from two aspects: Firstly, we propose a novel residual thermal infrared network (ResTNet) as the backbone in our method, which is designed to improve the feature extraction ability for low contrast targets by Transformer structure. Secondly, we propose a contrast enhancement loss function (CTEL) that optimizes the weights about the loss value of the low contrast targets’ prediction results to improve the effect of learning low contrast targets and compensate for the gradient of the low-contrast targets in training back propagation. Experiments on FLIR-ADAS dataset and our remote sensing dataset show that our method is far superior to the state-of-the-art ones in detecting low-contrast targets of IR images. The mAP of the proposed method reaches 84% on the FLIR public dataset. This is the best precision in published papers. Compared with the baseline, the performance on low-contrast targets is improved by about 20%. In addition, the proposed method is state-of-the-art on the FLIR dataset and our dataset. The comparative experiments demonstrate that our method has strong robustness and competitiveness. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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19 pages, 4876 KiB  
Article
Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image
by Qingyan Wang, Meng Chen, Junping Zhang, Shouqiang Kang and Yujing Wang
Remote Sens. 2022, 14(1), 171; https://doi.org/10.3390/rs14010171 - 31 Dec 2021
Cited by 8 | Viewed by 3212
Abstract
Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully applied in semi-supervised classification tasks [...] Read more.
Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully applied in semi-supervised classification tasks to address this problem, their performance inevitably meets the bottleneck due to the limitation of labeling cost. To address the aforementioned issue, this paper proposes a semi-supervised classification method for hyperspectral images that improves active deep learning. Specifically, the proposed model introduces the random multi-graph algorithm and replaces the expert mark in active learning with the anchor graph algorithm, which can label a considerable amount of unlabeled data precisely and automatically. In this way, a large number of pseudo-labeling samples would be added to the training subsets such that the model could be fine-tuned and the generalization performance could be improved without extra efforts for data manual labeling. Experiments based on three standard HSIs demonstrate that the proposed model can get better performance than other conventional methods, and they also outperform other studied algorithms in the case of a small training set. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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16 pages, 11433 KiB  
Article
Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets
by Yeping Peng, Zhen Tang, Genping Zhao, Guangzhong Cao and Chao Wu
Remote Sens. 2022, 14(1), 87; https://doi.org/10.3390/rs14010087 - 25 Dec 2021
Cited by 15 | Viewed by 3620
Abstract
Unmanned air vehicle (UAV) based imaging has been an attractive technology to be used for wind turbine blades (WTBs) monitoring. In such applications, image motion blur is a challenging problem which means that motion deblurring is of great significance in the monitoring of [...] Read more.
Unmanned air vehicle (UAV) based imaging has been an attractive technology to be used for wind turbine blades (WTBs) monitoring. In such applications, image motion blur is a challenging problem which means that motion deblurring is of great significance in the monitoring of running WTBs. However, an embarrassing fact for these applications is the lack of sufficient WTB images, which should include better pairs of sharp images and blurred images captured under the same conditions for network model training. To overcome the challenge of image pair acquisition, a training sample synthesis method is proposed. Sharp images of static WTBs were first captured, and then video sequences were prepared by running WTBs at different speeds. The blurred images were identified from the video sequences and matched to the sharp images using image difference. To expand the sample dataset, rotational motion blurs were simulated on different WTBs. Synthetic image pairs were then produced by fusing sharp images and images of simulated blurs. Finally, a total of 4000 image pairs were obtained. To conduct motion deblurring, a hybrid deblurring network integrated with DeblurGAN and DeblurGANv2 was deployed. The results show that the integration of DeblurGANv2 and Inception-ResNet-v2 provides better deblurred images, in terms of both metrics of signal-to-noise ratio (80.138) and structural similarity (0.950) than those obtained from the comparable networks of DeblurGAN and MobileNet-DeblurGANv2. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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21 pages, 10435 KiB  
Article
Multi-Source Remote Sensing Image Fusion for Ship Target Detection and Recognition
by Jinming Liu, Hao Chen and Yu Wang
Remote Sens. 2021, 13(23), 4852; https://doi.org/10.3390/rs13234852 - 29 Nov 2021
Cited by 22 | Viewed by 5179
Abstract
The active recognition of interesting targets has been a vital issue for remote sensing. In this paper, a novel multi-source fusion method for ship target detection and recognition is proposed. By introducing synthetic aperture radar (SAR) sensor images, the proposed method solves the [...] Read more.
The active recognition of interesting targets has been a vital issue for remote sensing. In this paper, a novel multi-source fusion method for ship target detection and recognition is proposed. By introducing synthetic aperture radar (SAR) sensor images, the proposed method solves the problem of precision degradation in optical remote sensing image target detection and recognition caused by the limit of illumination and weather conditions. The proposed method obtains port slice images containing ship targets by fusing optical data with SAR data. On this basis, spectral residual saliency and region growth method are used to detect ship targets in optical image, while SAR data are introduced to improve the accuracy of ship detection based on joint shape analysis and multi-feature classification. Finally, feature point matching, contour extraction and brightness saliency are used to detect the ship parts, and the ship target types are identified according to the voting results of part information. The proposed ship detection method obtained 91.43% recognition accuracy. The results showed that this paper provides an effective and efficient ship target detection and recognition method based on multi-source remote sensing images fusion. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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22 pages, 1713 KiB  
Article
A SINS/SAR/GPS Fusion Positioning System Based on Sensor Credibility Evaluations
by Maoyou Liao, Jiacheng Liu, Ziyang Meng and Zheng You
Remote Sens. 2021, 13(21), 4463; https://doi.org/10.3390/rs13214463 - 6 Nov 2021
Cited by 8 | Viewed by 3713
Abstract
A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit [...] Read more.
A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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23 pages, 8117 KiB  
Article
Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze
by Bo Jiang, Guanting Chen, Jinshuai Wang, Hang Ma, Lin Wang, Yuxuan Wang and Xiaoxuan Chen
Remote Sens. 2021, 13(21), 4443; https://doi.org/10.3390/rs13214443 - 4 Nov 2021
Cited by 18 | Viewed by 3489
Abstract
The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method [...] Read more.
The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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20 pages, 50318 KiB  
Article
A 3D Reconstruction Framework of Buildings Using Single Off-Nadir Satellite Image
by Chunhui Zhao, Chi Zhang, Yiming Yan and Nan Su
Remote Sens. 2021, 13(21), 4434; https://doi.org/10.3390/rs13214434 - 4 Nov 2021
Cited by 6 | Viewed by 3694
Abstract
A novel framework for 3D reconstruction of buildings based on a single off-nadir satellite image is proposed in this paper. Compared with the traditional methods of reconstruction using multiple images in remote sensing, recovering 3D information that utilizes the single image can reduce [...] Read more.
A novel framework for 3D reconstruction of buildings based on a single off-nadir satellite image is proposed in this paper. Compared with the traditional methods of reconstruction using multiple images in remote sensing, recovering 3D information that utilizes the single image can reduce the demands of reconstruction tasks from the perspective of input data. It solves the problem that multiple images suitable for traditional reconstruction methods cannot be acquired in some regions, where remote sensing resources are scarce. However, it is difficult to reconstruct a 3D model containing a complete shape and accurate scale from a single image. The geometric constraints are not sufficient as the view-angle, size of buildings, and spatial resolution of images are different among remote sensing images. To solve this problem, the reconstruction framework proposed consists of two convolutional neural networks: Scale-Occupancy-Network (Scale-ONet) and model scale optimization network (Optim-Net). Through reconstruction using the single off-nadir satellite image, Scale-Onet can generate water-tight mesh models with the exact shape and rough scale of buildings. Meanwhile, the Optim-Net can reduce the error of scale for these mesh models. Finally, the complete reconstructed scene is recovered by Model-Image matching. Profiting from well-designed networks, our framework has good robustness for different input images, with different view-angle, size of buildings, and spatial resolution. Experimental results show that an ideal reconstruction accuracy can be obtained both on the model shape and scale of buildings. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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15 pages, 10600 KiB  
Article
PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification
by Genping Zhao, Weiguang Zhang, Yeping Peng, Heng Wu, Zhuowei Wang and Lianglun Cheng
Remote Sens. 2021, 13(21), 4312; https://doi.org/10.3390/rs13214312 - 27 Oct 2021
Cited by 3 | Viewed by 2553
Abstract
Point cloud classification plays a significant role in Light Detection and Ranging (LiDAR) applications. However, most available multi-scale feature learning networks for large-scale 3D LiDAR point cloud classification tasks are time-consuming. In this paper, an efficient deep neural architecture denoted as Point Expanded [...] Read more.
Point cloud classification plays a significant role in Light Detection and Ranging (LiDAR) applications. However, most available multi-scale feature learning networks for large-scale 3D LiDAR point cloud classification tasks are time-consuming. In this paper, an efficient deep neural architecture denoted as Point Expanded Multi-scale Convolutional Network (PEMCNet) is developed to accurately classify the 3D LiDAR point cloud. Different from traditional networks for point cloud processing, PEMCNet includes successive Point Expanded Grouping (PEG) units and Absolute and Relative Spatial Embedding (ARSE) units for representative point feature learning. The PEG unit enables us to progressively increase the receptive field for each observed point and aggregate the feature of a point cloud at different scales but without increasing computation. The ARSE unit following the PEG unit furthermore realizes representative encoding of points relationship, which effectively preserves the geometric details between points. We evaluate our method on both public datasets (the Urban Semantic 3D (US3D) dataset and Semantic3D benchmark dataset) and our new collected Unmanned Aerial Vehicle (UAV) based LiDAR point cloud data of the campus of Guangdong University of Technology. In comparison with four available state-of-the-art methods, our methods ranked first place regarding both efficiency and accuracy. It was observed on the public datasets that with a 2% increase in classification accuracy, over 26% improvement of efficiency was achieved at the same time compared to the second efficient method. Its potential value is also tested on the newly collected point cloud data with over 91% of classification accuracy and 154 ms of processing time. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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28 pages, 13154 KiB  
Article
BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images
by Zhongzhen Sun, Xiangguang Leng, Yu Lei, Boli Xiong, Kefeng Ji and Gangyao Kuang
Remote Sens. 2021, 13(21), 4209; https://doi.org/10.3390/rs13214209 - 20 Oct 2021
Cited by 163 | Viewed by 10700
Abstract
Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense [...] Read more.
Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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22 pages, 11347 KiB  
Article
Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection
by Genping Zhao, Fei Li, Xiuwei Zhang, Kati Laakso and Jonathan Cheung-Wai Chan
Remote Sens. 2021, 13(20), 4102; https://doi.org/10.3390/rs13204102 - 13 Oct 2021
Cited by 12 | Viewed by 2700
Abstract
Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract [...] Read more.
Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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24 pages, 5331 KiB  
Article
Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages
by Genping Zhao, Arturo Sanchez-Azofeifa, Kati Laakso, Chuanliang Sun and Lunke Fei
Remote Sens. 2021, 13(19), 3830; https://doi.org/10.3390/rs13193830 - 24 Sep 2021
Cited by 6 | Viewed by 2561
Abstract
Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, [...] Read more.
Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged. Full article
(This article belongs to the Special Issue Object-Level Remote Sensing Image Information Extraction and Applications)
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