Change detection in remote sensing images based on coupled distance metric learning

Yan, Weidong; Hong, Jinfeng; Liu, Xinxin; Zhang, Sa

doi:10.1117/1.jrs.14.044506

Cited by 2 publications

(1 citation statement)

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“…As an aid, a DS module is introduced to improve the learning ability of the feature extractor and generate more useful features. Yan et al [49] proposed a coupled distance metric learning (CDML) model to enhance the contrast between changed and unchanged pixels. Zhan et al [50] performed CD by combining k-proximity and weighted contrastive loss.…”

Section: Related Work a Cnn-based Rscdmentioning

confidence: 99%

MDFENet: A Multiscale Difference Feature Enhancement Network for Remote Sensing Change Detection

Liu

et al. 2023

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

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The main task of remote sensing change detection (CD) is to identify object differences in bitemporal remote sensing images. In recent years, methods based on deep convolutional neural networks (CNNs) have made great progress in remote sensing CD. However, due to illumination changes and seasonal changes in the images acquired by the same sensor, the problem of "pseudo change" in the change map is still difficult to solve. In this article, in order to reduce "pseudo changes", we propose a multi-scale difference feature enhancement network (MDFENet) to extract the most discriminative features from bitemporal remote sensing images. MDFENet contains three procedures: first, multi-scale bitemporal features are generated by a shared weighted Siamese encoder. Then features of each scale are fed into a difference enhancement module to generate refined difference features. Finally, they are combined and reconstructed by a decoder to generate change map. The difference enhancement module includes multiple layers of difference enhancement (DE) encoder and transformer decoder. They are applied to features of different scales to establish long-range relationships of pixels semantic changes, while high-level difference features participate in the generation of low-level difference features to enhance information transmission among features of different scales, reducing "pseudo changes". Compared with state-of-the-art methods, the proposed method achieved the best performance on two datasets, with F1 of 81.15% on the SYSU-CD dataset and 90.85% on the LEVIR-CD dataset.

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Section: Related Work a Cnn-based Rscdmentioning

confidence: 99%

MDFENet: A Multiscale Difference Feature Enhancement Network for Remote Sensing Change Detection

Liu

et al. 2023

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

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MFMENet: multi-scale features mutual enhancement network for change detection in remote sensing images

Li,

Song,

et al. 2024

International Journal of Remote Sensing

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Change detection in remote sensing images based on coupled distance metric learning

Cited by 2 publications

References 0 publications

MDFENet: A Multiscale Difference Feature Enhancement Network for Remote Sensing Change Detection

MDFENet: A Multiscale Difference Feature Enhancement Network for Remote Sensing Change Detection

MFMENet: multi-scale features mutual enhancement network for change detection in remote sensing images

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