Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining the spectral correlation, spectral angle mapper (SAM), and Chebyshev distance. This index was then categorized into three groups of changed, unchanged, and uncertain classes using a fast multiple thresholding technique. In the fine stage, the subject image was first segmented into different clusters by the histogram-based fuzzy c-means (HFCM) algorithm. The optimal PIFs were then selected from unchanged and uncertain regions using each cluster’s bivariate joint distribution analysis. In the RRN modeling step, two normalized subject images were first produced using the robust linear regression (RLR) and cluster-wise-RLR (CRLR) methods based on the clustered PIFs. Finally, the normalized images were fused using the Choquet fuzzy integral fusion strategy for overwhelming the discontinuity between clusters in the final results and keeping the radiometric rectification optimal. Several experiments were implemented on four different bi-temporal satellite images and a simulated dataset to demonstrate the efficiency of the proposed method. The results showed that the proposed method yielded superior RRN results and outperformed other considered well-known RRN algorithms in terms of both accuracy level and execution time.publishedVersio

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

ES2Net: An Efficient Spectral-Spatial Network for Hyperspectral Image Change Detection

Hyperspectral image change detection (HSI-CD) aims to identify the differences in bitemporal HSIs. To mitigate spectral redundancy and improve the discriminativeness of changing features, some methods introduced band selection technology to select bands conducive for CD. However, these methods are limited by the inability to end-to-end training with the deep learning-based feature extractor and lack considering the complex nonlinear relationship among bands. In this paper, we propose an end-to-end efficient spectral-spatial change detection network (ES2Net) to address these issues. Specifically, we devised a learnable band selection module to automatically select bands conducive to CD. It can be jointly optimized with a feature extraction network and capture the complex nonlinear relationships among bands. Moreover, considering the large spatial feature distribution differences among different bands, we design the cluster-wise spatial attention mechanism that assigns a spatial attention factor to each individual band to individually improve the feature discriminativeness for each band. Experiments on three widely used HSI-CD datasets demonstrate the effectiveness and superiority of this method compared with other state-of-the-art methods

Local and Global Feature Learning With Kernel Scale-Adaptive Attention Network for VHR Remote Sensing Change Detection

© Copyright 2022 The Authors. Change detection is an important task of identifying changed information by comparing bitemporal images over the same geographical area. Currently, many existing methods based on U-Net and attention mechanism have greatly promoted the development of change detection techniques. However, they still suffer from two main challenges. First, faced with the diversity of ground objects and the flexibility of scale changes, vanilla attention mechanisms cripple spatial flexibility in learning object details due to the same scale convolution kernels at different convolution layers. Second, the complex background and high similarity between changed information and nonchanged information makes it difficult to fuse low-level details and high-level semantic by simple skip-connection in U-Net. To address the above issues, a local and global feature learning with kernel scale-adaptive attention network (LGSAA-Net) is proposed in this article. The proposed network makes two contributions. First, a scale-adaptive attention (SAA) module has been designed to exploit the relationships between feature maps and convolutional kernel scales. The SAA module can achieve better feature discrimination than vanilla attention mechanism. Second, a multilayer perceptron based on patches embedding has been employed by skip-connection to learn the local and global pixel association, which is helpful for achieving globally deep fusion of low-level details and high-level semantics. Finally, experiments and ablation studies are conducted on three datasets of LEVIR/WHU/GZ. Experimental results demonstrate that the proposed LGSAA-Net performs favorably against comparative current approaches and provides more accurate contour and better internal compactness for changed targets, thus verifying the effectiveness and superiority of the proposed LGSAA-Net in VHR remote sensing change detection.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61871259); Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47, 2022JQ-634 and 2022JQ-018); Key Research and Development Program of Shaanxi (Grant Number: 2022GY436, 2021ZDLGY08-07 and 2021GY-181); Shaanxi Joint Laboratory of Artificial Intelligence (Grant Number: 2020SS-03); Science and technology project of Xianyang city (Grant Number: 2021ZDZX-GY-0001)

UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation

Change detection (CD) by comparing two bi-temporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12\%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at \url{https://github.com/zhu-xlab/UCDFormer}Comment: 16 pages, 7 figures, IEEE Transactions on Geoscience and Remote Sensin

Difference Enhancement and Spatial-Spectral Non-Local Network for Change Detection in VHR Remote Sensing Images

Copyright © The Author(s) 2021. The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either ignore the original information of bitemporal images or insufficiently utilize the difference information between bitemporal images, which leads to the low tightness of the changed objects. Second, Siamese CNNs always employ dual-branch encoders for CD, which increases computational cost. To address the above issues, this article proposes a network based on difference enhancement and spatial–spectral nonlocal (DESSN) for CD in very-high-resolution (VHR) images. This article makes threefold contributions. First, we design a difference enhancement (DE) module that can effectively learn the difference representation between foreground and background to reduce the impact of irrelevant changes on the detection results. Second, we present a spatial–spectral nonlocal (SSN) module that is different from vanilla nonlocal because multiscale spatial global features are incorporated to model the large-scale variation of objects during CD. The module can be used to strengthen the edge integrity and internal tightness of changed objects. Third, the asymmetric double convolution with Ghost (ADCG) module is exploited instead of standard convolution. The ADCG can not only refine the edge information of the changed objects, since horizontal and vertical convolutional kernels have good contour preservation advantages, but also greatly reduce the computational complexity of the proposed model. The experiments on two public VHR CD datasets demonstrate that the proposed network can provide higher detection accuracy and requires smaller memory usage than state-of-the-art networks.10.13039/501100017596-Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61871259 and 61861024); 10.13039/501100000288-National Natural Science Foundation of China-Royal Society, U.K. (Grant Number: 61811530325 (IECnNSFCn170396)); Key Research and Development Program of Shaanxi (Grant Number: 2021ZDLGY08-07); Shaanxi Joint Laboratory of Artificial Intelligence (Grant Number: 2020SS-03); Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education

Performance analysis of change detection techniques for land use land cover

Remotely sensed satellite images have become essential to observe the spatial and temporal changes occurring due to either natural phenomenon or man-induced changes on the earth’s surface. Real time monitoring of this data provides useful information related to changes in extent of urbanization, environmental changes, water bodies, and forest. Through the use of remote sensing technology and geographic information system tools, it has become easier to monitor changes from past to present. In the present scenario, choosing a suitable change detection method plays a pivotal role in any remote sensing project. Previously, digital change detection was a tedious task. With the advent of machine learning techniques, it has become comparatively easier to detect changes in the digital images. The study gives a brief account of the main techniques of change detection related to land use land cover information. An effort is made to compare widely used change detection methods used to identify changes and discuss the need for development of enhanced change detection methods

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

Publisher's version (útgefin grein)In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial-spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial-spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial-spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial-spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.This research was funded by National Natural Science Foundation of China (Grant Number 41571346 and 61701396), the Natural Science Foundation of Shaan Xi Province (2018JQ4009), and the Open Fund for Key laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Natural Resource (Grant number SXDJ2017-10 and 2016KCT-23).Peer Reviewe

Ultralightweight Spatial-Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images

Copyright © The Author(s) 2023. Deep convolutional neural networks (CNNs) have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage. Second, the regular attention mechanism in CD is difficult to model spatial–spectral features and generate 3-D attention weights at the same time, ignoring the cooperation between spatial features and spectral features. To address the above issues, an efficient ultralightweight spatial–spectral feature cooperation network (USSFC-Net) is proposed for CD in this article. The proposed USSFC-Net has two main advantages. First, a multiscale decoupled convolution (MSDConv) is designed, which is clearly different from the popular atrous spatial pyramid pooling (ASPP) module and its variants since it can flexibly capture the multiscale features of changed objects using cyclic multiscale convolution. Meanwhile, the design of MSDConv can greatly reduce the number of parameters and computational redundancy. Second, an efficient spatial–spectral feature cooperation (SSFC) strategy is introduced to obtain richer features. The SSFC differs from the existing 2-D attention mechanisms since it learns 3-D spatial–spectral attention weights without adding any parameters. The experiments on three datasets for remote sensing image CD demonstrate that the proposed USSFC-Net achieves better CD accuracy than most CNNs-based methods and requires lower computational costs and fewer parameters, even it is superior to some Transformer-based methods. The code is available at https://github.com/SUST-reynole/USSFC-Net .10.13039/501100017596-Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47 and 2022JQ-592); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62271296, 62201452 and 62201334); 10.13039/501100015401-Key Research and Development Program of Shaanxi Province (Grant Number: 2022GY-436, 2021ZDLGY08-07 and 2021GY-181); Shaanxi Joint Laboratory of Artificial Intelligence (Grant Number: 2020SS-03); Scientific Research Program Funded by the Shaanxi Provincial Education Department (Grant Number: 22JK0568)