Continuous Change Detection of Flood Extents With Multisource Heterogeneous Satellite Image Time Series

Wang, Zhihao; Wang, Xueqian; Wu, Wei; Li, Gang

doi:10.1109/tgrs.2023.3281792

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…From 2021 some proposed approaches employ self-supervision [33,34], generating pseudo-labels to provide simple supervision information. Wang et al, proposed utilizing Markov Random Fields to conduct change detection on multi-source heterogeneous remote sensing images (primarily optical and SAR images) over long time series [35]. However, all the UDA methods mentioned above require access to fully annotated source data, which could be unavailable in real-world scenarios due to data privacy, security, and transmission limits.…”

Section: Unsupervised Domain Adaptation For Vhr Image Change Detectionmentioning

confidence: 99%

SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection

Wang,

2024

Remote Sensing

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Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model.

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Section: Unsupervised Domain Adaptation For Vhr Image Change Detectionmentioning

confidence: 99%

SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection

Wang,

2024

Remote Sensing

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A PSO-SVM-Based Change Detection Algorithm for Remote Sensing Optical Images

Shah,

Gupta,

Paul

2024

IEEE Access

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Change detection is considered as one of the challenging issues in the field of remote sensing. The multi-temporal images used to detect the changes generally have significant illumination variations which affect the performance of a change detection method. To address this issue, a machine learning (ML)based change detection algorithm is proposed for the remote sensing optical images. The proposed method is a combination of Support Vector Machines (SVM) and Particle Swarm Optimization (PSO). In this method, the PSO is utilized in a novel way to provide the optimized feature vectors. These feature vectors are used in SVM to accurately determine the changed and unchanged pixels. The proposed method is very effective in identifying the changes in remote sensing optical images having significant illumination variations. It can give comparatively higher correct classification (PCC) values, and lower false alarm (PFA) as well as total error (PTE) values than the state-of-the-art methods. Experiments on six pairs of Landsat images demonstrate the effectiveness of the proposed method.INDEX TERMS Machine Learning (ML), Particle Swarm Optimization (PSO) and Support Vector Machines (SVM).

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Progressive Difference Amplification Network With Edge Sensitivity for Remote Sensing Image Change Detection

Liang,

Xu,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Capturing finer and discriminative difference features (DF) is key to obtaining a high-quality change detection (CD) map. However, there is still significant scope for further study on fine-grained detection, especially concerning terms of improving structural integrity and reducing internal holes or sticking in DF. To this end, we propose a progressive difference amplification network (PDANet) with edge sensitivity to detect changed areas in optical remote sensing images (RSI), where the key point is to amplify DF and reinforce edge detail to improve CD accuracy. The edge sensitivity encoder (ES) is designed to capture the long-distance dependency, which compensates for the limited receptive fields of the convolutional neural network with fixed kernels. Meanwhile, we introduce the prior edge in the network training stage, which collaborates with the ESE to improve the structural integrity of the changed areas. On the other hand, the difference amplification decoder is proposed to enhance the representation of the changed areas, and it is achieved by integrating multi-scale DF and reconstructing the original single RSI using DF as full-stage guidance. Finally, the CD map and edge map are predicted based on the reconstructed feature and the maximum scale DF. Extensive experiments on one instance dataset and three CD benchmark datasets demonstrate that PDANet outperforms the state-of-the-art CD competitors both qualitatively and quantitatively.

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Continuous Change Detection of Flood Extents With Multisource Heterogeneous Satellite Image Time Series

Cited by 5 publications

References 51 publications

SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection

SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection

A PSO-SVM-Based Change Detection Algorithm for Remote Sensing Optical Images

Progressive Difference Amplification Network With Edge Sensitivity for Remote Sensing Image Change Detection

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