MapsNet: Multi-level feature constraint and fusion network for change detection

Pan, Jianping; Cui, Wei; An, Xinyong; Huang, Xiao; Zhang, Hanchao; Zhang, Sihang; Zhang, Ruiqian; Li, Xin; Cheng, Weihua; Hu, Yong

doi:10.1016/j.jag.2022.102676

Cited by 21 publications

(7 citation statements)

References 26 publications

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“…Ma et al 43 introduced coordinate attention to obtaining more accurate position information and channel relationship. Pan et al 44 used convolutional block attention modules (CBAM) to learn channel and spatial information in features, improving the model's ability to locate the identical locations between bitemporal images and determine changes in small objects. However, these methods only consider the attention mechanism in the channel and space dimension, which ignores the relationship in the time dimension.…”

Section: Attention Mechanism In Change Detection Networkmentioning

confidence: 99%

CCSNet: a cascaded cross-scale network for remote sensing image change detection

Xue

Liu

et al. 2023

J. Appl. Rem. Sens.

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Remote sensing image (RSI) analysis heavily relies on change detection (CD) to identify differences between bitemporal images. Although significant progress has been made in CD methods, it remains challenging to distinguish between pseudochanges caused by imaging angles, weather conditions, seasonal variations, etc. This is because most existing networks cannot fully utilize multi-scale features to narrow the semantic gap between cross-scale features. To address this issue, we propose a cascade cross-scale network (CCSNet) with a Siamese structure for CD in RSIs. Our CCSNet uses cascade Siamese encoders to extract multi-scale bitemporal features. These extracted features are then fused using the proposed Diff-Conc combination at the encoding stage. Cross-scale skip connections are used to continuously transmit shallow features to the decoders, in which a crossscale attention module is designed to fuse shallow and deep features. This network structure effectively bridges the semantic gap between cross-scale features and helps eliminate pseudo-changes. We evaluated our method on two popular benchmark CD datasets (LEVIR-CD and WHU-CD). The experimental results demonstrate that CCSNet outperforms the state-of-the-art methods.

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Section: Attention Mechanism In Change Detection Networkmentioning

confidence: 99%

CCSNet: a cascaded cross-scale network for remote sensing image change detection

Xue

Liu

et al. 2023

J. Appl. Rem. Sens.

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“…For example, multi-level feature constraint and fusion networks (Pan et al, 2022) and deeply supervised image fusion networks (DSIFN) (Zhang et al, 2020) have demonstrated the strength of image fusion for LCCD with RSIs.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, some networks aim at fusing the deep features of bitemporal images (Song et al., 2022), which means that the advantage of image fusion lies in making full use of the information of bitemporal images (Wen et al., 2021). For example, multi‐level feature constraint and fusion networks (Pan et al., 2022) and deeply supervised image fusion networks (DSIFN) (Zhang et al., 2020) have demonstrated the strength of image fusion for LCCD with RSIs.…”

Section: Introductionmentioning

confidence: 99%

Cross‐attention neural network for land cover change detection with remote sensing images

Lv,

Zhong,

Wang

et al. 2024

The Photogrammetric Record

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Land cover change detection (LCCD) with remote sensing images (RSIs) is important for observing the land cover change of the Earth's surface. Considering the insufficient performance of the traditional self‐attention mechanism used in a neural network to smoothen the noise of LCCD with RSIs, in this study a novel cross‐attention neural network (CANN) was proposed for improving the performance of LCCD with RSIs. In the proposed CANN, a cross‐attention mechanism was achieved by employing another temporal image to enhance attention performance and improve detection accuracies. First, a feature difference module was embedded in the backbone of the proposed CANN to generate a change magnitude image and guide the learning progress. A self‐attention module based on the cross‐attention mechanism was then proposed and embedded in the encoder of the proposed network to make the network pay attention to the changed area. Finally, the encoded features were decoded to obtain binary change detection with the ArgMax function. Compared with five methods, the experimental results based on six pairs of real RSIs well demonstrated the feasibility and superiority of the proposed network for achieving LCCD with RSIs. For example, the improvement for overall accuracy for the six pairs of real RSIs improved by our proposed approach is about 0.72–2.56%.

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“…In addition, deep learning is receiving much attention in different computer vision research areas, including the analysis of remote sensing images change detection [36][37][38][39]. Deep features of pixels or objects are extracted through deep learning methods, such as the neural network of spatial-temporal attention [14], the transformer-based model [40], and fully convolutional two-stream architecture [41]. Meanwhile, to better aggregate contextual and detailed information from remote sensing images, some researchers introduced feature fusion networks for change detection [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

“…Deep features of pixels or objects are extracted through deep learning methods, such as the neural network of spatial-temporal attention [14], the transformer-based model [40], and fully convolutional two-stream architecture [41]. Meanwhile, to better aggregate contextual and detailed information from remote sensing images, some researchers introduced feature fusion networks for change detection [41][42][43]. It should be noted that deep learning-based methods require a certain number of labelled training samples.…”

Section: Introductionmentioning

confidence: 99%

Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images

Ding

Molina

et al. 2022

Remote Sensing

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Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection.

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MapsNet: Multi-level feature constraint and fusion network for change detection

Cited by 21 publications

References 26 publications

CCSNet: a cascaded cross-scale network for remote sensing image change detection

CCSNet: a cascaded cross-scale network for remote sensing image change detection

Cross‐attention neural network for land cover change detection with remote sensing images

Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images

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