Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference

Peng, Xueli; Zhong, Rugang; Li, Zhen; Li, Qingyang

doi:10.1109/tgrs.2020.3033009

Cited by 256 publications

(138 citation statements)

References 44 publications

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“…This network achieves the robustness in detection recall. Based on UNet++, Peng et al [37] use skip connection inside convolution unit, to emphasis the difference learning by additional skip connections. During upsampling, they use a spatial and channel attentive upsampling unit to better locate detailed information and texture features, which further improves the performance.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

Zhang

et al. 2021

Remote Sensing

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Accurate change detection in optical aerial images by using deep learning techniques has been attracting lots of research efforts in recent years. Correct change-detection results usually involve both global and local deep learning features. Existing deep learning approaches have achieved good performance on this task. However, under the scenarios of containing multiscale change areas within a bi-temporal image pair, existing methods still have shortcomings in adapting these change areas, such as false detection and limited completeness in detected areas. To deal with these problems, we design a hierarchical dynamic fusion network (HDFNet) to implement the optical aerial image-change detection task. Specifically, we propose a change-detection framework with hierarchical fusion strategy to provide sufficient information encouraging for change detection and introduce dynamic convolution modules to self-adaptively learn from this information. Also, we use a multilevel supervision strategy with multiscale loss functions to supervise the training process. Comprehensive experiments are conducted on two benchmark datasets, LEBEDEV and LEVIR-CD, to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

Zhang

et al. 2021

Remote Sensing

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“…Instead, some supervised CD methods attempt to apply CNNs trained in an end-to-end manner through the available labeled samples. Specifically, most existing CD networks adopt the U-shaped encoder-decoder architecture, where the encoder consists of an early- [28], [29], [37] or late-fusion [10], [30]- [35] framework for feature extraction. The former takes the concatenated bitemporal images as an input, whereas the latter extracts features from the two images in parallel.…”

Section: Related Workmentioning

confidence: 99%

“…Alternatively, the attention mechanism [52] has been widely studied and embedded into deep CNNs in many computer vision tasks [53]- [62]. To the best of our knowledge, works that utilize the attention mechanism for CD based on RS images are not sufficient [6], [35], [37], [38], [63], [64]. Specifically, the self-attention mechanism [53] is effective in modeling longrange dependencies and generating discriminative features.…”

Section: Related Workmentioning

confidence: 99%

AGCDetNet:An Attention-Guided Network for Building Change Detection in High-Resolution Remote Sensing Images

Song

Jiang

2021

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

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While deep learning-based methods have gained considerable improvements in remote sensing (RS) image change detection (CD), scale variations and pseudo-changes hinder most supervised methods' performance. The CD networks derived from other fields can be fronted with false alarms and miss detections in high-resolution RS images due to the weak feature representation ability. In this paper, an attention-guided end-to-end change detection network (AGCDetNet) is proposed based on the fully convolutional network and attention mechanism. AGCDetNet learns to enhance the feature representation of change information and achieve accuracy improvements using spatial-and channelattention. A spatial attention module (SPAM) promotes the discrimination between the changed objects and the background by adding the learned spatial attention to the deep features. Channel-wise attention-guided interference filtering unit (CIFU) / atrous spatial pyramid pooling module (CG-ASPP) enhances the representation of multilevel features and multiscale context, respectively. Extensive experiments have been conducted on several public datasets for performance evaluation, including LEVIR-CD, WHU, Season-Varying, WV2, and ZY3. Experiment results demonstrate that AGCDetNet outperforms several stateof-the-art methods of accuracy and robustness. Specifically, AGCDetNet achieves the best F1-score on two datasets, i.e., LEVIR-CD (0.9076) and Season-Varying (0.9654).

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“…2) Comparisons on LEVIR-CD. W-Net [3], FC-EF-Res [4], and Peng et al [5], and attention-based methods STANet [6], DDCNN [7], and FarSeg [17] were selected as benchmarks. In particular, STANet was proposed by the dataset's creator.…”

Section: Comparisons With Other Approaches 1) Comparisons Of Netwomentioning

confidence: 99%

“…Moreover, due to some works adopting different datasets for evaluation, it is difficult to say which one achieves the best performance. Although some methods are evaluated on the same dataset, unfortunately, they adopt different criteria to split the datasets [6], [7], which makes it difficult to compare them directly. In addition, only a few works are openly available to the public.…”

Section: Introductionmentioning

confidence: 99%

3M-CDNet: A Universal Powerful Benchmark for Remote Sensing Image Change Detection

Song¹,

Jiang²

2021

Preprint

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Abstract—While deep learning-based methods have gained popularity and have made remarkable progress in remote sensing (RS) image change detection (CD), the limited amount of available data hinders the performance of most supervised methods. The CD networks transferred or derived from other fields can be fronted with a weak generalization capability. Developing a universal benchmark for performance evaluations based on the available datasets is urgent. To address these problems, we proposed a lightweight network, termed 3M-CDNet, which only requires about 3.12 M parameters. The lighter the network, the easier it is to train and alleviate overfitting the limited amount of data, resulting in a better generalization capability. 3M-CDNet has a flexible modular design that achieves performance improvements by incorporating plug-and-play modules. 3M-CDNet gains accuracy improvements in two ways: (1) the application of deformable convolutions (DConv) in the backbone network to gain a good geometric transformation modeling capacity for CD and (2) the application of an effective two-level feature fusion strategy to enhance the feature representation capacity. 3M-CDNet gains a good generalization capacity by incorporating effective “tricks” to alleviate overfitting, in which online data augmentation (Online DA) is applied to increase the diversity of the training samples, and Dropout regularization is applied in the classifier. Extensive ablation studies have proved the effectiveness of the core components. Experiment results suggest that 3M-CDNet outperforms state-of-the-art methods on several optical RS datasets and serves as a new universal benchmark. Specifically, 3M-CDNet achieves the best F1-score, i.e., LEVIR-CD (0.9161), Season-Varying (0.9473), and DSIFN (0.7031).

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Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference

Cited by 256 publications

References 44 publications

HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

AGCDetNet:An Attention-Guided Network for Building Change Detection in High-Resolution Remote Sensing Images

3M-CDNet: A Universal Powerful Benchmark for Remote Sensing Image Change Detection

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