Context and Difference Enhancement Network for Change Detection

Song, Dawei; Dong, Yongsheng; Li, Xuelong

doi:10.1109/jstars.2022.3217082

Cited by 9 publications

(3 citation statements)

References 62 publications

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“…Zhang et al [29] proposed the feature difference module based on different receptive fields to rich change information. Song et al [30] inserted a spatial attention-based content difference enhancement module into middleware between the encoder and decoder to refine the encoded features. All of the above work achieves difference enhancement based on encoded features.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…Compared with visible and multispectral imagery, hyperspectral images (HSIs) contain hundreds of continuous spectral bands that provide spectral-spatial information. On this basis, HSI interpretation techniques have been widely applied in many fields, such as classification, 2 – 5 change detection, 6 – 9 and object detection 10 – 12 As an important subbranch of hyperspectral target location, hyperspectral anomaly detection (HAD) aims to distinguish objects of interest by capturing subtle spectral differences of ground objects without any prior information or supervision 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Wang,

Shi,

Cheng

et al. 2023

J. Appl. Rem. Sens.

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Hyperspectral anomaly detection (HAD) is a technique to find observations without prior knowledge, which is of particular interest as a branch of remote sensing object detection. However, the application of HAD is limited by various challenges, such as high-dimensional data, high intraclass variability, redundant information, and limited samples. To overcome these restrictions, we report an unsupervised strategy to implement HAD by dimensionality reduction (DR) and prior-based collaborative representation with adaptive global salient weight. The proposed framework includes three main steps. First, we select the most discriminating bands as the input hyperspectral images for subsequent processing in a DR manner. Then, we apply piecewise-smooth prior and local salient prior to collaborative representation to produce the initial detection map. Finally, to generate the final detection map, a global adaptive salient map is applied to the initial anomaly map to further highlight anomalies. Most importantly, the experimental results show that the proposed method outperforms alternative detectors on several datasets over different scenes. In particular, on the Gulfport dataset, the area under the curve value obtained by the proposed method is 0.9932, which is higher than the second-best method, convolutional neural network detector, by 0.0071.

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“…Since deep-learning-based methods can obtain discrimination in the space of latent semantic features, they have emerged as a prominent area of research in recent years. To date, deep learning has tackled numerous challenging issues in the field of computer vision [45,46]. In a supervised manner, based on the convolutional neural network (CNN) and fully connected layer, Li et al [47] explored the performance of transfer learning for HAD.…”

Section: Introductionmentioning

confidence: 99%

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection

Wang,

Shi,

et al. 2023

Remote Sensing

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Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the “curse of dimensionality”, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ℓ2,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.

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Context and Difference Enhancement Network for Change Detection

Cited by 9 publications

References 62 publications

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

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

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection

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