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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 March 2019) | Viewed by 80947

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Prof. Dr. Fabio Del Frate

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Department of Civil Engineering and Computer Science Engineering, University of Rome “Tor Vergata”, Via del Politecnico, 1, 00133 Rome, Italy
Interests: applications of machine learning to remote sensing; image processing; retrieval of atmospheric variables from satellite data; SAR applications; hyperspectral data processing

Dr. Javier Plaza Miguel

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Department of Computer Technology and Communications, University of Extremadura, Avda. Universidad s/n, 10071 Cáceres, Spain
Interests: hyperspectral image analysis; signal processing; deep learning; neural networks and high performance computing

Special Issue Information

Dear Colleagues,

The amount of data being produced by remote-sensing techniques is increasing exponentially. This is, not only due to the continuous investments of space missions, but also to the breakthroughs caused by devices used on Unmanned Aerial Vehicles. Moreover, additional data enhancement is provided by the latest technological advances in the fields of sensors, in terms of storage possibilities and transfer capabilities. In this scenario, imagery analytics and interpretation—which are often still performed by human experts, require an increase in the level of automation in the process of value-added generation from data. In such a context, artificial intelligence based on neural networks, with the inherent capability of identifying nonlinear relationships among data, may have a leading role. Indeed, neural networks can be efficient for different tasks, not only for statistical regressions or image classification, but also for image segmentation, feature extraction, data fusion or dimensionality reduction. The goal of this Special Issue is to collect the latest and most advanced ideas regarding the implementation of neural networks algorithms for next-generation geospatial applications. In addition to contributions addressing issues regarding conceptual models, such as deep learning or neuromorphic computing, papers regarding new results in the migration of neural networks in high-performing computing environments, or for intelligent onboard processing, will be welcomed.

Prof. Fabio Del Frate
Prof. Javier Plaza Miguel
Guest Editors

Manuscript Submission Information

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Keywords

Neural Networks
High Performance Computing
Automatic Onboard Processing
Automatic Image Processing
Artificial Intelligence for Remote Sensing
Data Science
Unmanned Aerial Vehicles
Deep Learning

Published Papers (13 papers)

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Research

18 pages, 3010 KiB

Open AccessArticle

Adversarial Reconstruction-Classification Networks for PolSAR Image Classification

by Yanqiao Chen, Yangyang Li, Licheng Jiao, Cheng Peng, Xiangrong Zhang and Ronghua Shang

Remote Sens. 2019, 11(4), 415; https://doi.org/10.3390/rs11040415 - 18 Feb 2019

Cited by 12 | Viewed by 2990

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more widely used in recent years. It is well known that PolSAR image classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR image classification. Nevertheless, for FCN, there are some problems to solve in PolSAR image classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR image classification. However, only when the labeled training sample is sufficient, can SFCN achieve good classification results. To address the above mentioned problem, we propose adversarial reconstruction-classification networks (ARCN), which is based on SFCN and introduces reconstruction-classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised image classification and unsupervised image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true image and reconstructed image can be detected and revised. Our method can achieve impressive performance in PolSAR image classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR images demonstrate the efficiency of the proposed method. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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23 pages, 10782 KiB

Open AccessArticle

Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

by Nan Mo, Li Yan, Ruixi Zhu and Hong Xie

Remote Sens. 2019, 11(3), 272; https://doi.org/10.3390/rs11030272 - 30 Jan 2019

Cited by 17 | Viewed by 4032

Abstract

In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-class object detection method with a convolutional neural network (CNN), which can be divided into two parts: a class-specific anchor based region proposal network (RPN) and a discriminative feature with a context information classification network. A class-specific anchor block providing better initial values for RPN is proposed to generate the anchor of the most suitable scale for each category in order to increase the recall ratio. Meanwhile, we proposed to incorporate the context information into the original convolutional feature to improve the discriminative ability of the features and increase classification accuracy. Considering the quality of samples for classification, the soft filter is proposed to select effective boxes to improve the diversity of the samples for the classifier and avoid missing or false detection to some extent. We also introduced the focal loss in order to improve the classifier in classifying the hard samples. The proposed method is tested on a benchmark dataset of ten classes to prove the superiority. The proposed method outperforms some state-of-the-art methods with a mean average precision (mAP) of 90.4% and better detects the multi-scale objects, especially when objects show a minor shape change. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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17 pages, 4557 KiB

Open AccessArticle

A Novel Deep Fully Convolutional Network for PolSAR Image Classification

by Yangyang Li, Yanqiao Chen, Guangyuan Liu and Licheng Jiao

Remote Sens. 2018, 10(12), 1984; https://doi.org/10.3390/rs10121984 - 07 Dec 2018

Cited by 36 | Viewed by 5241

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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18 pages, 7337 KiB

Open AccessArticle

Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery

by Mohamad M. Al Rahhal, Yakoub Bazi, Taghreed Abdullah, Mohamed L. Mekhalfi, Haikel AlHichri and Mansour Zuair

Remote Sens. 2018, 10(12), 1890; https://doi.org/10.3390/rs10121890 - 27 Nov 2018

Cited by 35 | Viewed by 5659

Abstract

In this paper we propose a multi-branch neural network, called MB-Net, for solving the problem of knowledge adaptation from multiple remote sensing scene datasets acquired with different sensors over diverse locations and manually labeled with different experts. Our aim is to learn invariant feature representations from multiple source domains with labeled images and one target domain with unlabeled images. To this end, we define for MB-Net an objective function that mitigates the multiple domain shifts at both feature representation and decision levels, while retaining the ability to discriminate between different land-cover classes. The complete architecture is trainable end-to-end via the backpropagation algorithm. In the experiments, we demonstrate the effectiveness of the proposed method on a new multiple domain dataset created from four heterogonous scene datasets well known to the remote sensing community, namely, the University of California (UC-Merced) dataset, the Aerial Image dataset (AID), the PatternNet dataset, and the Northwestern Polytechnical University (NWPU) dataset. In particular, this method boosts the average accuracy over all transfer scenarios up to 89.05% compared to standard architecture based only on cross-entropy loss, which yields an average accuracy of 78.53%. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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27 pages, 8852 KiB

Open AccessArticle

Ground and Multi-Class Classification of Airborne Laser Scanner Point Clouds Using Fully Convolutional Networks

by Aldino Rizaldy, Claudio Persello, Caroline Gevaert, Sander Oude Elberink and George Vosselman

Remote Sens. 2018, 10(11), 1723; https://doi.org/10.3390/rs10111723 - 31 Oct 2018

Cited by 39 | Viewed by 5221

Abstract

Various classification methods have been developed to extract meaningful information from Airborne Laser Scanner (ALS) point clouds. However, the accuracy and the computational efficiency of the existing methods need to be improved, especially for the analysis of large datasets (e.g., at regional or national levels). In this paper, we present a novel deep learning approach to ground classification for Digital Terrain Model (DTM) extraction as well as for multi-class land-cover classification, delivering highly accurate classification results in a computationally efficient manner. Considering the top–down acquisition angle of ALS data, the point cloud is initially projected on the horizontal plane and converted into a multi-dimensional image. Then, classification techniques based on Fully Convolutional Networks (FCN) with dilated kernels are designed to perform pixel-wise image classification. Finally, labels are transferred from pixels to the original ALS points. We also designed a Multi-Scale FCN (MS-FCN) architecture to minimize the loss of information during the point-to-image conversion. In the ground classification experiment, we compared our method to a Convolutional Neural Network (CNN)-based method and LAStools software. We obtained a lower total error on both the International Society for Photogrammetry and Remote Sensing (ISPRS) filter test benchmark dataset and AHN-3 dataset in the Netherlands. In the multi-class classification experiment, our method resulted in higher precision and recall values compared to the traditional machine learning technique using Random Forest (RF); it accurately detected small buildings. The FCN achieved precision and recall values of 0.93 and 0.94 when RF obtained 0.91 and 0.92, respectively. Moreover, our strategy significantly improved the computational efficiency of state-of-the-art CNN-based methods, reducing the point-to-image conversion time from 47 h to 36 min in our experiments on the ISPRS filter test dataset. Misclassification errors remained in situations that were not included in the training dataset, such as large buildings and bridges, or contained noisy measurements. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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28 pages, 1920 KiB

Open AccessArticle

Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

by Mercedes E. Paoletti, Juan M. Haut, Javier Plaza and Antonio Plaza

Remote Sens. 2018, 10(9), 1454; https://doi.org/10.3390/rs10091454 - 11 Sep 2018

Cited by 88 | Viewed by 10284

Abstract

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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25 pages, 5332 KiB

Open AccessArticle

Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework

by Sankaranarayanan Piramanayagam, Eli Saber, Wade Schwartzkopf and Frederick W. Koehler

Remote Sens. 2018, 10(9), 1429; https://doi.org/10.3390/rs10091429 - 07 Sep 2018

Cited by 69 | Viewed by 8072

Abstract

In this paper, we present a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also propose a composite fusion architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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18 pages, 8088 KiB

Open AccessArticle

A Multiple-Feature Reuse Network to Extract Buildings from Remote Sensing Imagery

by Lin Li, Jian Liang, Min Weng and Haihong Zhu

Remote Sens. 2018, 10(9), 1350; https://doi.org/10.3390/rs10091350 - 24 Aug 2018

Cited by 66 | Viewed by 5407

Abstract

Automatic building extraction from remote sensing imagery is important in many applications. The success of convolutional neural networks (CNNs) has also led to advances in using CNNs to extract man-made objects from high-resolution imagery. However, the large appearance and size variations of buildings make it difficult to extract both crowded small buildings and large buildings. High-resolution imagery must be segmented into patches for CNN models due to GPU memory limitations, and buildings are typically only partially contained in a single patch with little context information. To overcome the problems involved when using different levels of image features with common CNN models, this paper proposes a novel CNN architecture called a multiple-feature reuse network (MFRN) in which each layer is connected to all the subsequent layers of the same size, enabling the direct use of the hierarchical features in each layer. In addition, the model includes a smart decoder that enables precise localization with less GPU load. We tested our model on a large real-world remote sensing dataset and obtained an overall accuracy of 94.5% and an 85% F1 score, which outperformed the compared CNN models, including a 56-layer fully convolutional DenseNet with 93.8% overall accuracy and an F1 score of 83.5%. The experimental results indicate that the MFRN approach to connecting convolutional layers improves the performance of common CNN models for extracting buildings of different sizes and can achieve high accuracy with a consumer-level GPU. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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22 pages, 14334 KiB

Open AccessArticle

Sentinel-2 Image Fusion Using a Deep Residual Network

by Frosti Palsson, Johannes R. Sveinsson and Magnus O. Ulfarsson

Remote Sens. 2018, 10(8), 1290; https://doi.org/10.3390/rs10081290 - 15 Aug 2018

Cited by 42 | Viewed by 8344

Abstract

Single sensor fusion is the fusion of two or more spectrally disjoint reflectance bands that have different spatial resolution and have been acquired by the same sensor. An example is Sentinel-2, a constellation of two satellites, which can acquire multispectral bands of 10 m, 20 m and 60 m resolution for visible, near infrared (NIR) and shortwave infrared (SWIR). In this paper, we present a method to fuse the fine and coarse spatial resolution bands to obtain finer spatial resolution versions of the coarse bands. It is based on a deep convolutional neural network which has a residual design that models the fusion problem. The residual architecture helps the network to converge faster and allows for deeper networks by relieving the network of having to learn the coarse spatial resolution part of the inputs, enabling it to focus on constructing the missing fine spatial details. Using several real Sentinel-2 datasets, we study the effects of the most important hyperparameters on the quantitative quality of the fused image, compare the method to several state-of-the-art methods and demonstrate that it outperforms the comparison methods in experiments. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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21 pages, 12337 KiB

Open AccessArticle

Improved Fully Convolutional Network with Conditional Random Fields for Building Extraction

by Sanjeevan Shrestha and Leonardo Vanneschi

Remote Sens. 2018, 10(7), 1135; https://doi.org/10.3390/rs10071135 - 18 Jul 2018

Cited by 96 | Viewed by 8207

Abstract

Building extraction from remotely sensed imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Several published contributions dedicated to the applications of deep convolutional neural networks (DCNN) for building extraction using aerial/satellite imagery exists. However, in all these contributions, high accuracy is always obtained at the price of extremely complex and large network architectures. In this paper, we present an enhanced fully convolutional network (FCN) framework that is designed for building extraction of remotely sensed images by applying conditional random fields (CRFs). The main objective is to propose a methodology selecting a framework that balances high accuracy with low network complexity. A modern activation function, namely, the exponential linear unit (ELU), is applied to improve the performance of the fully convolutional network (FCN), thereby resulting in more accurate building prediction. To further reduce the noise (falsely classified buildings) and to sharpen the boundaries of the buildings, a post-processing conditional random fields (CRFs) is added at the end of the adopted convolutional neural network (CNN) framework. The experiments were conducted on Massachusetts building aerial imagery. The results show that our proposed framework outperformed the fully convolutional network (FCN), which is the existing baseline framework for semantic segmentation, in terms of performance measures such as the F1-score and IoU measure. Additionally, the proposed method outperformed a pre-existing classifier for building extraction using the same dataset in terms of the performance measures and network complexity. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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17 pages, 14613 KiB

Open AccessArticle

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

by Tianyang Shi, Qizhi Xu, Zhengxia Zou and Zhenwei Shi

Remote Sens. 2018, 10(7), 1130; https://doi.org/10.3390/rs10071130 - 18 Jul 2018

Cited by 47 | Viewed by 3917

Abstract

Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop’s growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea–land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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20 pages, 41363 KiB

Open AccessArticle

High-Resolution Remote Sensing Image Classification Method Based on Convolutional Neural Network and Restricted Conditional Random Field

by Xin Pan and Jian Zhao

Remote Sens. 2018, 10(6), 920; https://doi.org/10.3390/rs10060920 - 10 Jun 2018

Cited by 33 | Viewed by 7562

Abstract

Convolutional neural networks (CNNs) can adapt to more complex data, extract deeper characteristics from images, and achieve higher classification accuracy in remote sensing image scene classification and object detection compared to traditional shallow-model methods. However, directly applying common-structure CNNs to pixel-based remote sensing image classification will lead to boundary or outline distortions of the land cover and consumes enormous computation time in the image classification stage. To solve this problem, we propose a high-resolution remote sensing image classification method based on CNN and the restricted conditional random field algorithm (CNN-RCRF). CNN-RCRF adopts CNN superpixel classification instead of pixel-based classification and uses the restricted conditional random field algorithm (RCRF) to refine the superpixel result image into a pixel-based result. The proposed method not only takes advantage of the classification ability of CNNs but can also avoid boundary or outline distortions of the land cover and greatly reduce computation time in classifying images. The effectiveness of the proposed method is tested with two high-resolution remote sensing images, and the experimental results show that the CNN-RCRF outperforms the existing traditional methods in terms of overall accuracy, and CNN-RCRF’s computation time is much less than that of traditional pixel-based deep-model methods. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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15 pages, 1054 KiB

Open AccessArticle

Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

by Shuang Liu, Mei Li, Zhong Zhang, Baihua Xiao and Xiaozhong Cao

Remote Sens. 2018, 10(6), 822; https://doi.org/10.3390/rs10060822 - 25 May 2018

Cited by 38 | Viewed by 4028

Abstract

The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task. Full article

(This article belongs to the Special Issue Recent Advances in Neural Networks for Remote Sensing)

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