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Deep Reinforcement Learning in Remote Sensing Image Processing

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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 (15 February 2023) | Viewed by 24076

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Prof. Dr. Kun Tan

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Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Interests: remote sensing image processing; image classification and detection; deep learning; spectral unmixing

Prof. Dr. Jie Feng

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Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an 710071, China
Interests: deep learning; object detection and tracking; reinforcement learning; hyperspectral image processing
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Prof. Dr. Qian Du

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Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Xue Wang

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Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
Interests: hyperspectral image processing and classification; deep learning; land surface inversion; soil property

Special Issue Information

Dear Colleagues,

Remote sensing is currently a rapidly developing field of Earth observation. The captured remote sensing image cubes provide abundant information, which shows great research prospects in many different Earth applications, such as crop management, vegetation monitoring, object identification, mineral identification, anomaly detection, land cover type classification, mapping, etc. However, the limited prior knowledge of the labelled samples and the high redundancy of spectral information bring great challenges to the development of remote sensing. Recently, deep reinforcement learning, which closely combines deep learning and reinforcement learning, has demonstrated significant achievements in various fields, while there is still a lack of application cases of remote sensing image processing combined with deep reinforcement learning. In this Special Issue, we aim to compile a collection of state-of-the-art research on the application of remote sensing.

This Special Issue aims to capture advances and trends in the application of deep reinforcement learning in image processing. Specifically, the topics of interest include but are not limited to the suggested themes below.

Band selection, dimensionality reduction
Remote Sensing image processing
Ensemble algorithms with reinforcement learning
Deep reinforcement learning

Prof. Dr. Kun Tan
Dr. Jie Feng
Prof. Dr. Qian Du
Dr. Xue Wang
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing image classification
remote sensing image denoising
remote sensing image pan-sharpening
object/anomaly detection of hyperspectral images
reinforcement learning
model-based reinforcement learning
multi-agent reinforcement learning
deep reinforcement learning

Published Papers (10 papers)

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Research

23 pages, 6213 KiB

Open AccessArticle

Hyperspectral Feature Selection for SOM Prediction Using Deep Reinforcement Learning and Multiple Subset Evaluation Strategies

by Linya Zhao, Kun Tan, Xue Wang, Jianwei Ding, Zhaoxian Liu, Huilin Ma and Bo Han

Remote Sens. 2023, 15(1), 127; https://doi.org/10.3390/rs15010127 - 26 Dec 2022

Cited by 6 | Viewed by 1754

Abstract

It has been widely certified that hyperspectral images can be effectively used to monitor soil organic matter (SOM). Though numerous bands reveal more details in spectral features, information redundancy and noise interference also come accordingly. Due to the fact that, nowadays, prevailing dimensionality reduction methods targeted to hyperspectral images fail to make effective band selections, it is hard to capture the spectral features of ground objects quickly and accurately. In this paper, to solve the inefficiency and instability of hyperspectral feature selection, we proposed a feature selection framework named reinforcement learning for feature selection in hyperspectral regression (RLFSR). Specifically, the Markov Decision Process (MDP) was used to simulate the hyperspectral band selection process, and reinforcement learning agents were introduced to improve model performance. Then two spectral feature evaluation methods were introduced to find internal relationships between the hyperspectral features and thus comprehensively evaluate all hyperspectral bands aimed at the soil. The feature selection methods—RLFSR-Net and RLFSR-Cv—were based on pre-trained deep networks and cross-validation, respectively, and achieved excellent results on airborne hyperspectral images from Yitong Manchu Autonomous County in China. The feature subsets achieved the highest accuracy for most inversion models, with inversion R² values of 0.7506 and 0.7518, respectively. The two proposed methods showed slight differences in spectral feature extraction preferences and hyperspectral feature selection flexibilities in deep reinforcement learning. The experiments showed that the proposed RLFSR framework could better capture the spectral characteristics of SOM than the existing methods. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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34 pages, 22212 KiB

Open AccessArticle

Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising

by Huiqing Qi, Shengli Tan and Zhichao Li

Remote Sens. 2022, 14(24), 6300; https://doi.org/10.3390/rs14246300 - 12 Dec 2022

Cited by 5 | Viewed by 1637

Abstract

Remote sensing images are widely applied in instance segmentation and objetive recognition; however, they often suffer from noise, influencing the performance of subsequent applications. Previous image denoising works have only obtained restored images without preserving detailed texture. To address this issue, we proposed [...] Read more.

F^{2}

Net), consisting of four novel modules (WTV-Net, SOSB, AuEncoder, and FB). AWTV

F^{2}

Net combines traditional total variation with a deep neural network, improving the denoising ability of the proposed approach. Our proposed method is evaluated by PSNR and SSIM metrics on three benchmark datasets (NWPU, PatternNet, UCL), and the experimental results show that AWTV

F^{2}

Net can obtain 0.12∼19.39 dB/0.0237∼0.5362 higher on PSNR/SSIM values in the Gaussian noise removal and mixed noise removal tasks than State-of-The-Art (SoTA) algorithms. Meanwhile, our model can preserve more detailed texture features. The SSEQ, BLIINDS-II, and BRISQUE values of AWTV

F^{2}

Net on the three real-world datasets (AVRIS Indian Pines, ROSIS University of Pavia, HYDICE Urban) are 3.94∼12.92 higher, 8.33∼27.5 higher, and 2.2∼5.55 lower than those of the compared methods, respectively. The proposed framework can guide subsequent remote sensing image applications, regarding the pre-processing of input images. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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24 pages, 51305 KiB

Open AccessArticle

DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing Images

by Wenqing Wang, Zhiqiang Zhou, Xiaoqiao Zhang, Tu Lv, Han Liu and Lili Liang

Remote Sens. 2022, 14(23), 6120; https://doi.org/10.3390/rs14236120 - 02 Dec 2022

Cited by 1 | Viewed by 1259

Abstract

Pansharpening is one of the main research topics in the field of remote sensing image processing. In pansharpening, the spectral information from a low spatial resolution multispectral (LRMS) image and the spatial information from a high spatial resolution panchromatic (PAN) image are integrated to obtain a high spatial resolution multispectral (HRMS) image. As a prerequisite for the application of LRMS and PAN images, pansharpening has received extensive attention from researchers, and many pansharpening methods based on convolutional neural networks (CNN) have been proposed. However, most CNN-based methods regard pansharpening as a super-resolution reconstruction problem, which may not make full use of the feature information in two types of source images. Inspired by the PanNet model, this paper proposes a detail injection-based two-branch network (DiTBN) for pansharpening. In order to obtain the most abundant spatial detail features, a two-branch network is designed to extract features from the high-frequency component of the PAN image and the multispectral image. Moreover, the feature information provided by source images is reused in the network to further improve information utilization. In order to avoid the training difficulty for a real dataset, a new loss function is introduced to enhance the spectral and spatial consistency between the fused HRMS image and the input images. Experiments on different datasets show that the proposed method achieves excellent performance in both qualitative and quantitative evaluations as compared with several advanced pansharpening methods. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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16 pages, 6906 KiB

Open AccessArticle

Aerial Image Dehazing Using Reinforcement Learning

by Jing Yu, Deying Liang, Bo Hang and Hongtao Gao

Remote Sens. 2022, 14(23), 5998; https://doi.org/10.3390/rs14235998 - 26 Nov 2022

Cited by 4 | Viewed by 1878

Abstract

Aerial observation is usually affected by the Earth’s atmosphere, especially when haze exists. Deep reinforcement learning was used in this study for dehazing. We first developed a clear–hazy aerial image dataset addressing various types of ground; we then compared the dehazing results of some state-of-the-art methods, including the classic dark channel prior, color attenuation prior, non-local image dehazing, multi-scale convolutional neural networks, DehazeNet, and all-in-one dehazing network. We extended the most suitable method, DehazeNet, to a multi-scale form and added it into a multi-agent deep reinforcement learning network called DRL_Dehaze. DRL_Dehaze was tested on several ground types and in situations with multiple haze scales. The results show that each pixel agent can automatically select the most suitable method in multi-scale haze situations and can produce a good dehazing result. Different ground scenes may best be processed using different steps. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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19 pages, 3023 KiB

Open AccessArticle

Remote Sensing Image-Change Detection with Pre-Generation of Depthwise-Separable Change-Salient Maps

by Bin Li, Guanghui Wang, Tao Zhang, Huachao Yang and Shubi Zhang

Remote Sens. 2022, 14(19), 4972; https://doi.org/10.3390/rs14194972 - 06 Oct 2022

Viewed by 1458

Abstract

Remote sensing change detection (CD) identifies changes in each pixel of certain classes of interest from a set of aligned image pairs. It is challenging to accurately identify natural changes in feature categories due to unstructured and temporal changes. This research proposed an effective bi-temporal remote sensing CD comprising an encoder that could extract multiscale features, a decoder that focused on semantic alignment between temporal features, and a classification head. In the decoder, we constructed a new convolutional attention structure based on pre-generation of depthwise-separable change-salient maps (PDACN) that could reduce the attention of the network on unchanged regions and thus reduce the potential pseudo-variation in the data sources caused by semantic differences in illumination and subtle alignment differences. To demonstrate the effectiveness of the PDA attention structure, we designed a lightweight network structure for encoders under both convolution-based and transformer architectures. The experiments were conducted on a single-building CD dataset (LEVIR-CD) and a more complex multivariate change type dataset (SYSU-CD). The results showed that our PDA attention structure generated more discriminative change variance information while the entire network model obtained the best performance results with the same level of network model parameters in the transformer architecture. For LEVIR-CD, we achieved an intersection over union (IoU) of 0.8492 and an F1 score of 0.9185. For SYSU-CD, we obtained an IoU of 0.7028 and an F1 score of 0.8255. The experimental results showed that the method proposed in this paper was superior to some current state-of-the-art CD methods. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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23 pages, 2551 KiB

Open AccessArticle

MSAC-Net: 3D Multi-Scale Attention Convolutional Network for Multi-Spectral Imagery Pansharpening

by Erlei Zhang, Yihao Fu, Jun Wang, Lu Liu, Kai Yu and Jinye Peng

Remote Sens. 2022, 14(12), 2761; https://doi.org/10.3390/rs14122761 - 08 Jun 2022

Cited by 5 | Viewed by 1808

Abstract

Pansharpening fuses spectral information from the multi-spectral image and spatial information from the panchromatic image, generating super-resolution multi-spectral images with high spatial resolution. In this paper, we proposed a novel 3D multi-scale attention convolutional network (MSAC-Net) based on the typical U-Net framework for multi-spectral imagery pansharpening. MSAC-Net is designed via 3D convolution, and the attention mechanism replaces the skip connection between the contraction and expansion pathways. Multiple pansharpening layers at the expansion pathway are designed to calculate the reconstruction results for preserving multi-scale spatial information. The MSAC-Net performance is verified on the IKONOS and QuickBird satellites’ datasets, proving that MSAC-Net achieves comparable or superior performance to the state-of-the-art methods. Additionally, 2D and 3D convolution are compared, and the influences of the number of convolutions in the convolution block, the weight of multi-scale information, and the network’s depth on the network performance are analyzed. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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17 pages, 4528 KiB

Open AccessArticle

A Network Combining a Transformer and a Convolutional Neural Network for Remote Sensing Image Change Detection

by Guanghui Wang, Bin Li, Tao Zhang and Shubi Zhang

Remote Sens. 2022, 14(9), 2228; https://doi.org/10.3390/rs14092228 - 06 May 2022

Cited by 35 | Viewed by 3933

Abstract

With the development of deep learning techniques in the field of remote sensing change detection, many change detection algorithms based on convolutional neural networks (CNNs) and nonlocal self-attention (NLSA) mechanisms have been widely used and have obtained good detection accuracy. However, these methods mainly extract semantic features on images from different periods without taking into account the temporal dependence between these features. This will lead to more “pseudo-change” in complex scenes. In this paper, we propose a network architecture named UVACD for bitemporal image change detection. The network combines a CNNs extraction backbone for extracting high-level semantic information with a visual transformer. Here, visual transformer constructs change intensity tokens to complete the temporal information interaction and suppress irrelevant information weights to help extract more distinguishable change features. Our network is validated and tested on both the LEVIR-CD and WHU datasets. For the LEVIR-CD dataset, we achieve an intersection over union (IoU) of 0.8398 and an F1 score of 0.9130. For the WHU dataset, we achieve an IoU of 0.8664 and an F1 score of 0.9284. The experimental results show that the proposed method outperforms some previous state of the art change detection methods. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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20 pages, 32549 KiB

Open AccessArticle

Multiscale Spatial–Spectral Interaction Transformer for Pan-Sharpening

by Feng Zhang, Kai Zhang and Jiande Sun

Remote Sens. 2022, 14(7), 1736; https://doi.org/10.3390/rs14071736 - 04 Apr 2022

Cited by 19 | Viewed by 2158

Abstract

Pan-sharpening methods based on deep neural network (DNN) have produced state-of-the-art fusion performance. However, DNN-based methods mainly focus on the modeling of the local properties in low spatial resolution multispectral (LR MS) and panchromatic (PAN) images by convolution neural networks. The global dependencies in the images are ignored. To capture the local and global properties of the images concurrently, we propose a multiscale spatial–spectral interaction transformer (MSIT) for pan-sharpening. Specifically, we construct the multiscale sub-networks containing convolution–transformer encoder to extract the local and global features at different scales from LR MS and PAN images, respectively. Then, a spatial–spectral interaction attention module (SIAM) is designed to merge the features at each scale. In SIAM, the interaction attention is used to decouple the spatial and spectral information efficiently for the enhancement of complementarity and the reduction of redundancy in the extracted features. The features from different scales are further integrated into a multiscale reconstruction module (MRM) to generate the desired high spatial resolution multispectral image, in which the spatial and spectral information is fused scale by scale. The experiments on reduced- and full-scale datasets demonstrate that the proposed MSIT can produce better results in terms of visual and numerical analysis when compared with state-of-the-art methods. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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21 pages, 6173 KiB

Open AccessArticle

Hyperspectral Image Classification Based on 3D Coordination Attention Mechanism Network

by Cuiping Shi, Diling Liao, Tianyu Zhang and Liguo Wang

Remote Sens. 2022, 14(3), 608; https://doi.org/10.3390/rs14030608 - 27 Jan 2022

Cited by 19 | Viewed by 2991

Abstract

In recent years, due to its powerful feature extraction ability, the deep learning method has been widely used in hyperspectral image classification tasks. However, the features extracted by classical deep learning methods have limited discrimination ability, resulting in unsatisfactory classification performance. In addition, due to the limited data samples of hyperspectral images (HSIs), how to achieve high classification performance under limited samples is also a research hotspot. In order to solve the above problems, this paper proposes a deep learning network framework named the three-dimensional coordination attention mechanism network (3DCAMNet). In this paper, a three-dimensional coordination attention mechanism (3DCAM) is designed. This attention mechanism can not only obtain the long-distance dependence of the spatial position of HSIs in the vertical and horizontal directions, but also obtain the difference of importance between different spectral bands. In order to extract the spectral and spatial information of HSIs more fully, a convolution module based on convolutional neural network (CNN) is adopted in this paper. In addition, the linear module is introduced after the convolution module, which can extract more fine advanced features. In order to verify the effectiveness of 3DCAMNet, a series of experiments were carried out on five datasets, namely, Indian Pines (IP), Pavia University (UP), Kennedy Space Center (KSC), Salinas Valley (SV), and University of Houston (HT). The OAs obtained by the proposed method on the five datasets were 95.81%, 97.01%, 99.01%, 97.48%, and 97.69% respectively, 3.71%, 9.56%, 0.67%, 2.89% and 0.11% higher than the most advanced A2S2K-ResNet. Experimental results show that, compared with some state-of-the-art methods, 3DCAMNet not only has higher classification performance, but also has stronger robustness. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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27 pages, 17706 KiB

Open AccessArticle

A Lightweight Convolutional Neural Network Based on Channel Multi-Group Fusion for Remote Sensing Scene Classification

by Cuiping Shi, Xinlei Zhang and Liguo Wang

Remote Sens. 2022, 14(1), 9; https://doi.org/10.3390/rs14010009 - 21 Dec 2021

Cited by 9 | Viewed by 2579

Abstract

With the development of remote sensing scene image classification, convolutional neural networks have become the most commonly used method in this field with their powerful feature extraction ability. In order to improve the classification performance of convolutional neural networks, many studies extract deeper features by increasing the depth and width of convolutional neural networks, which improves classification performance but also increases the complexity of the model. To solve this problem, a lightweight convolutional neural network based on channel multi-group fusion (LCNN-CMGF) is presented. For the proposed LCNN-CMGF method, a three-branch downsampling structure was designed to extract shallow features from remote sensing images. In the deep layer of the network, the channel multi-group fusion structure is used to extract the abstract semantic features of remote sensing scene images. The structure solves the problem of lack of information exchange between groups caused by group convolution through channel fusion of adjacent features. The four most commonly used remote sensing scene datasets, UCM21, RSSCN7, AID and NWPU45, were used to carry out a variety of experiments in this paper. The experimental results under the conditions of four datasets and multiple training ratios show that the proposed LCNN-CMGF method has more significant performance advantages than the compared advanced method. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Remote Sensing Image Processing)

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