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Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 32666

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Prof. Dr. Chunhui Zhao

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College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; intelligent information processing
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Prof. Dr. Jinchang Ren

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The National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK
Interests: remote sensing; image processing; hyperspectral image processing; pattern recognition
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Prof. Dr. Leyuan Fang

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College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: deep learning; remote sensing image processing and analysis
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Prof. Dr. Weiwei Sun

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Department of Geography and Spatial Information Techniques, Ningbo University Beijing, Ningbo 315211, China
Interests: remote sensing; hyperspectral image processing; artificial intelligence
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Dr. Shou Feng

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College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; data mining; machine learning
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Dr. Nan Su

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College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: multimodal object detection and tracking
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Dr. Yiming Yan

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College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001,China
Interests: multi-modal information extraction; image retrieval; 3D modeling
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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 spearheaded current research. With the rapid development of artificial intelligence technology, many new remote sensing image processing methods and algorithms 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 about the artificial intelligence algorithm for remote sensing imagery processing. Papers on both theoretical methods and applicative techniques, as well as contributions regarding new advanced methodologies on relevant scenarios of remote sensing images, are welcome. We look forward to receiving your contributions.

Prof. Dr. Chunhui Zhao
Prof. Dr. Jinchang Ren
Prof. Dr. Leyuan Fang
Prof. Dr. Weiwei Sun
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
optical/multispectral/hyperspectral image processing
LiDAR
SAR
target detection, anomaly detection, and change 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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Related Special Issue

Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III in Remote Sensing (15 articles)

Published Papers (13 papers)

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20 pages, 9204 KiB

Open AccessArticle

A Gated Content-Oriented Residual Dense Network for Hyperspectral Image Super-Resolution

by Jing Hu, Tingting Li, Minghua Zhao, Fei Wang and Jiawei Ning

Remote Sens. 2023, 15(13), 3378; https://doi.org/10.3390/rs15133378 - 2 Jul 2023

Cited by 1 | Viewed by 1260

Abstract

Limited by the existing imagery sensors, a hyperspectral image (HSI) is characterized by its high spectral resolution but low spatial resolution. HSI super-resolution (SR) aims to enhance the spatial resolution of the HSIs without modifying the equipment and has become a hot issue for HSI processing. In this paper, inspired by two important observations, a gated content-oriented residual dense network (GCoRDN) is designed for the HSI SR. To be specific, based on the observation that the structure and texture exhibit different sensitivities to the spatial degradation, a content-oriented network with two branches is designed. Meanwhile, a weight-sharing strategy is merged in the network to preserve the consistency in the structure and the texture. In addition, based on the observation of the super-resolved results, a gating mechanism is applied as a form of post-processing to further enhance the SR performance. Experimental results and data analysis on both ground-based HSIs and airborne HSIs have demonstrated the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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24 pages, 8002 KiB

Open AccessArticle

Automatic ISAR Ship Detection Using Triangle-Points Affine Transform Reconstruction Algorithm

by Xinfei Jin, Fulin Su, Hongxu Li, Zihan Xu and Jie Deng

Remote Sens. 2023, 15(10), 2507; https://doi.org/10.3390/rs15102507 - 10 May 2023

Cited by 4 | Viewed by 1962

Abstract

With the capability of capturing a target’s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target’s attitude can lead to the scatterers’ rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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18 pages, 4759 KiB

Open AccessArticle

Multi-Scale Ship Detection Algorithm Based on YOLOv7 for Complex Scene SAR Images

by Zhuo Chen, Chang Liu, V. F. Filaretov and D. A. Yukhimets

Remote Sens. 2023, 15(8), 2071; https://doi.org/10.3390/rs15082071 - 14 Apr 2023

Cited by 52 | Viewed by 6501

Abstract

Recently, deep learning techniques have been extensively used to detect ships in synthetic aperture radar (SAR) images. The majority of modern algorithms can achieve successful ship detection outcomes when working with multiple-scale ships on a large sea surface. However, there are still issues, such as missed detection and incorrect identification when performing multi-scale ship object detection operations in SAR images of complex scenes. To solve these problems, this paper proposes a complex scenes multi-scale ship detection model, according to YOLOv7, called CSD-YOLO. First, this paper suggests an SAS-FPN module that combines atrous spatial pyramid pooling and shuffle attention, allowing the model to focus on important information and ignore irrelevant information, reduce the feature loss of small ships, and simultaneously fuse the feature maps of ship targets on various SAR image scales, thereby improving detection accuracy and the model’s capacity to detect objects at several scales. The model’s optimization is then improved with the aid of the SIoU loss function. Finally, thorough tests on the HRSID and SSDD datasets are presented to support our methodology. CSD-YOLO achieves better detection performance than the baseline YOLOv7, with a 98.01% detection accuracy, a 96.18% recall, and a mean average precision (mAP) of 98.60% on SSDD. In addition, in comparative experiments with other deep learning-based methods, in terms of overall performance, CSD-YOLO still performs better. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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27 pages, 42556 KiB

Open AccessArticle

Space Target Material Identification Based on Graph Convolutional Neural Network

by Na Li, Chengeng Gong, Huijie Zhao and Yun Ma

Remote Sens. 2023, 15(7), 1937; https://doi.org/10.3390/rs15071937 - 4 Apr 2023

Cited by 4 | Viewed by 2563

Abstract

Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85–90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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28 pages, 5366 KiB

Open AccessArticle

Adaptive-Attention Completing Network for Remote Sensing Image

by Wenli Huang, Ye Deng, Siqi Hui and Jinjun Wang

Remote Sens. 2023, 15(5), 1321; https://doi.org/10.3390/rs15051321 - 27 Feb 2023

Cited by 6 | Viewed by 2349

Abstract

The reconstruction of missing pixels is essential for remote sensing images, as they often suffer from problems such as covering, dead pixels, and scan line corrector (SLC)-off. Image inpainting techniques can solve these problems, as they can generate realistic content for the unknown regions of an image based on the known regions. Recently, convolutional neural network (CNN)-based inpainting methods have integrated the attention mechanism to improve inpainting performance, as they can capture long-range dependencies and adapt to inputs in a flexible manner. However, to obtain the attention map for each feature, they compute the similarities between the feature and the entire feature map, which may introduce noise from irrelevant features. To address this problem, we propose a novel adaptive attention (Ada-attention) that uses an offset position subnet to adaptively select the most relevant keys and values based on self-attention. This enables the attention to be focused on essential features and model more informative dependencies on the global range. Ada-attention first employs an offset subnet to predict offset position maps on the query feature map; then, it samples the most relevant features from the input feature map based on the offset position; next, it computes key and value maps for self-attention using the sampled features; finally, using the query, key and value maps, the self-attention outputs the reconstructed feature map. Based on Ada-attention, we customized a u-shaped adaptive-attention completing network (AACNet) to reconstruct missing regions. Experimental results on several digital remote sensing and natural image datasets, using two image inpainting models and two remote sensing image reconstruction approaches, demonstrate that the proposed AACNet achieves a good quantitative performance and good visual restoration results with regard to object integrity, texture/edge detail, and structural consistency. Ablation studies indicate that Ada-attention outperforms self-attention in terms of PSNR by 0.66%, SSIM by 0.74%, and MAE by 3.9%, and can focus on valuable global features using the adaptive offset subnet. Additionally, our approach has also been successfully applied to remove real clouds in remote sensing images, generating credible content for cloudy regions. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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28 pages, 7900 KiB

Open AccessArticle

Two-Branch Convolutional Neural Network with Polarized Full Attention for Hyperspectral Image Classification

by Haimiao Ge, Liguo Wang, Moqi Liu, Yuexia Zhu, Xiaoyu Zhao, Haizhu Pan and Yanzhong Liu

Remote Sens. 2023, 15(3), 848; https://doi.org/10.3390/rs15030848 - 2 Feb 2023

Cited by 16 | Viewed by 2792

Abstract

In recent years, convolutional neural networks (CNNs) have been introduced for pixel-wise hyperspectral image (HSI) classification tasks. However, some problems of the CNNs are still insufficiently addressed, such as the receptive field problem, small sample problem, and feature fusion problem. To tackle the above problems, we proposed a two-branch convolutional neural network with a polarized full attention mechanism for HSI classification. In the proposed network, two-branch CNNs are implemented to efficiently extract the spectral and spatial features, respectively. The kernel sizes of the convolutional layers are simplified to reduce the complexity of the network. This approach can make the network easier to be trained and fit the network to small sample size conditions. The one-shot connection technique is applied to improve the efficiency of feature extraction. An improved full attention block, named polarized full attention, is exploited to fuse the feature maps and provide global contextual information. Experimental results on several public HSI datasets confirm the effectiveness of the proposed network. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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24 pages, 4897 KiB

Open AccessArticle

Image Inpainting with Bilateral Convolution

by Wenli Huang, Ye Deng, Siqi Hui and Jinjun Wang

Remote Sens. 2022, 14(23), 6140; https://doi.org/10.3390/rs14236140 - 3 Dec 2022

Cited by 3 | Viewed by 2915

Abstract

Due to sensor malfunctions and poor atmospheric conditions, remote sensing images often miss important information/pixels, which affects downstream tasks, therefore requiring reconstruction. Current image reconstruction methods use deep convolutional neural networks to improve inpainting performances as they have a powerful modeling capability. However, deep convolutional networks learn different features with the same group of convolutional kernels, which restricts their ability to handle diverse image corruptions and often results in color discrepancy and blurriness in the recovered images. To mitigate this problem, in this paper, we propose an operator called Bilateral Convolution (BC) to adaptively preserve and propagate information from known regions to missing data regions. On the basis of vanilla convolution, the BC dynamically propagates more confident features, which weights the input features of a patch according to their spatial location and feature value. Furthermore, to capture different range dependencies, we designed a Multi-range Window Attention (MWA) module, in which the input feature is divided into multiple sizes of non-overlapped patches for several heads, and then these feature patches are processed by the window self-attention. With BC and MWA, we designed a bilateral convolution network for image inpainting. We conducted experiments on remote sensing datasets and several typical image inpainting datasets to verify the effectiveness and generalization of our network. The results show that our network adaptively captures features between known and unknown regions, generates appropriate content for various corrupted images, and has a competitive performance compared with state-of-the-art methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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22 pages, 6172 KiB

Open AccessArticle

Improved Central Attention Network-Based Tensor RX for Hyperspectral Anomaly Detection

by Lili Zhang, Jiachen Ma, Baohong Fu, Fang Lin, Yudan Sun and Fengpin Wang

Remote Sens. 2022, 14(22), 5865; https://doi.org/10.3390/rs14225865 - 19 Nov 2022

Viewed by 1398

Abstract

Recently, using spatial–spectral information for hyperspectral anomaly detection (AD) has received extensive attention. However, the test point and its neighborhood points are usually treated equally without highlighting the test point, which is unreasonable. In this paper, improved central attention network-based tensor RX (ICAN-TRX) is designed to extract hyperspectral anomaly targets. The ICAN-TRX algorithm consists of two parts, ICAN and TRX. In ICAN, a test tensor block as a value tensor is first reconstructed by DBN to make the anomaly points more prominent. Then, in the reconstructed tensor block, the central tensor is used as a convolution kernel to perform convolution operation with its tensor block. The result tensor as a key tensor is transformed into a weight matrix. Finally, after the correlation operation between the value tensor and the weight matrix, the new test point is obtained. In ICAN, the spectral information of a test point is emphasized, and the spatial relationships between the test point and its neighborhood points reflect their similarities. TRX is used in the new HSI after ICAN, which allows more abundant spatial information to be used for AD. Five real hyperspectral datasets are selected to estimate the performance of the proposed ICAN-TRX algorithm. The detection results demonstrate that ICAN-TRX achieves superior performance compared with seven other AD algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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24 pages, 7021 KiB

Open AccessArticle

A Change Detection Method Based on Multi-Scale Adaptive Convolution Kernel Network and Multimodal Conditional Random Field for Multi-Temporal Multispectral Images

by Shou Feng, Yuanze Fan, Yingjie Tang, Hao Cheng, Chunhui Zhao, Yaoxuan Zhu and Chunhua Cheng

Remote Sens. 2022, 14(21), 5368; https://doi.org/10.3390/rs14215368 - 26 Oct 2022

Cited by 16 | Viewed by 2067

Abstract

Multispectral image change detection is an important application in the field of remote sensing. Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. The multi-scale adaptive kernel network (MSAK-Net) extends the encoding path of the U-Net, and designs a weight-sharing bilateral encoding path, which simultaneously extracts independent features of bi-temporal multispectral images without introducing additional parameters. A selective convolution kernel block (SCKB) that can adaptively assign weights is designed and embedded in the encoding path of MSAK-Net to extract multi-scale features in images. MSAK-Net retains the skip connections in the U-Net, and embeds an upsampling module (UM) based on the attention mechanism in the decoding path, which can give the feature map a better expression of change information in both the channel dimension and the spatial dimension. Finally, the multimodal conditional random field (MCRF) is used to smooth the detection results of the MSAK-Net. Experimental results on two public multispectral datasets indicate the effectiveness and robustness of the proposed method when compared with other state-of-the-art methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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20 pages, 9685 KiB

Open AccessArticle

Gully Erosion Monitoring Based on Semi-Supervised Semantic Segmentation with Boundary-Guided Pseudo-Label Generation Strategy and Adaptive Loss Function

by Chunhui Zhao, Yi Shen, Nan Su, Yiming Yan and Yong Liu

Remote Sens. 2022, 14(20), 5110; https://doi.org/10.3390/rs14205110 - 13 Oct 2022

Cited by 5 | Viewed by 1755

Abstract

Gully erosion is a major threat to ecosystems, potentially leading to desertification, land degradation, and crop loss. Developing viable gully erosion prevention and remediation strategies requires regular monitoring of the gullies. Nevertheless, it is highly challenging to automatically access the monitoring results of the gullies from the latest monitoring data by training historical data acquired by different sensors at different times. To this end, this paper presents a novel semi-supervised semantic segmentation with boundary-guided pseudo-label generation strategy and adaptive loss function method. This method takes full advantage of the historical data with labels and the latest monitoring data without labels to obtain the latest monitoring results of the gullies. The boundary-guided pseudo-label generation strategy (BPGS), guided by the inherent boundary maps of real geographic objects, fuses multiple evidence data to generate reliable pseudo-labels. Additionally, we propose an adaptive loss function based on centroid similarity (CSIM) to further alleviate the impact of pseudo-label noise. To verify the proposed method, two datasets for gully erosion monitoring are constructed according to the satellite data acquired in northeastern China. Extensive experiments demonstrate that the proposed method is more appropriate for automatic gully erosion monitoring than four state-of-the-art methods, including supervised methods and semi-supervised methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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31 pages, 17920 KiB

Open AccessArticle

A Novel Hybrid Attention-Driven Multistream Hierarchical Graph Embedding Network for Remote Sensing Object Detection

by Shu Tian, Lin Cao, Lihong Kang, Xiangwei Xing, Jing Tian, Kangning Du, Ke Sun, Chunzhuo Fan, Yuzhe Fu and Ye Zhang

Remote Sens. 2022, 14(19), 4951; https://doi.org/10.3390/rs14194951 - 4 Oct 2022

Cited by 2 | Viewed by 1907

Abstract

Multiclass geospatial object detection in high-spatial-resolution remote-sensing images (HSRIs) has recently attracted considerable attention in many remote-sensing applications as a fundamental task. However, the complexity and uncertainty of spatial distribution among multiclass geospatial objects are still huge challenges for object detection in HSRIs. Most current remote-sensing object-detection approaches fall back on deep convolutional neural networks (CNNs). Nevertheless, most existing methods only focus on mining visual characteristics and lose sight of spatial or semantic relation discriminations, eventually degrading object-detection performance in HSRIs. To tackle these challenges, we propose a novel hybrid attention-driven multistream hierarchical graph embedding network (HA-MHGEN) to explore complementary spatial and semantic patterns for improving remote-sensing object-detection performance. Specifically, we first constructed hierarchical spatial graphs for multiscale spatial relation representation. Then, semantic graphs were also constructed by integrating them with the word embedding of object category labels on graph nodes. Afterwards, we developed a self-attention-aware multiscale graph convolutional network (GCN) to derive stronger for intra- and interobject hierarchical spatial relations and contextual semantic relations, respectively. These two relation networks were followed by a novel cross-attention-driven spatial- and semantic-feature fusion module that utilizes a multihead attention mechanism to learn associations between diverse spatial and semantic correlations, and guide them to endowing a more powerful discrimination ability. With the collaborative learning of the three relation networks, the proposed HA-MHGEN enables grasping explicit and implicit relations from spatial and semantic patterns, and boosts multiclass object-detection performance in HRSIs. Comprehensive and extensive experimental evaluation results on three benchmarks, namely, DOTA, DIOR, and NWPU VHR-10, demonstrate the effectiveness and superiority of our proposed method compared with that of other advanced remote-sensing object-detection methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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16 pages, 4639 KiB

Open AccessTechnical Note

SG-Det: Shuffle-GhostNet-Based Detector for Real-Time Maritime Object Detection in UAV Images

by Lili Zhang, Ning Zhang, Rui Shi, Gaoxu Wang, Yi Xu and Zhe Chen

Remote Sens. 2023, 15(13), 3365; https://doi.org/10.3390/rs15133365 - 30 Jun 2023

Cited by 4 | Viewed by 1590

Abstract

Maritime search and rescue is a crucial component of the national emergency response system, which mainly relies on unmanned aerial vehicles (UAVs) to detect objects. Most traditional object detection methods focus on boosting the detection accuracy while neglecting the detection speed of the heavy model. However, improving the detection speed is essential, which can provide timely maritime search and rescue. To address the issues, we propose a lightweight object detector named Shuffle-GhostNet-based detector (SG-Det). First, we construct a lightweight backbone named Shuffle-GhostNet, which enhances the information flow between channel groups by redesigning the correlation group convolution and introducing the channel shuffle operation. Second, we propose an improved feature pyramid model, namely BiFPN-tiny, which has a lighter structure capable of reinforcing small object features. Furthermore, we incorporate the Atrous Spatial Pyramid Pooling module (ASPP) into the network, which employs atrous convolution with different sampling rates to obtain multi-scale information. Finally, we generate three sets of bounding boxes at different scales—large, medium, and small—to detect objects of different sizes. Compared with other lightweight detectors, SG-Det achieves better tradeoffs across performance metrics and enables real-time detection with an accuracy rate of over 90% for maritime objects, showing that it can better meet the actual requirements of maritime search and rescue. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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15 pages, 10804 KiB

Open AccessTechnical Note

Noised Phase Unwrapping Based on the Adaptive Window of Wigner Distribution

by Junqiu Chu, Xingling Liu, Haotong Ma, Xuegang Yu and Ge Ren

Remote Sens. 2022, 14(21), 5603; https://doi.org/10.3390/rs14215603 - 6 Nov 2022

Viewed by 1770

Abstract

A noised phase-unwrapping method is presented by using the Wigner distribution function to filter the phase noise and restore the gradient of the phase map. By using Poisson’s equation, the unwrapped phase map was obtained. Compared with the existing methods, the proposed method is theoretically simple, provides a more accurate representation, and can be implemented in light-field hardware devices, such as Shack-Hartmann sensors. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ)

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