A Deep Siamese Postclassification Fusion Network for Semantic Change Detection

Xia, Hao; Tian, Yugang; Zhang, Lihao; Li, Shuangliang

doi:10.1109/tgrs.2022.3171067

Cited by 36 publications

(12 citation statements)

References 60 publications

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“…Similarly, Zhu et al [32] proposed a Twin Global Learning Framework (Siam-GL), incorporating twin networks, G-H sampling mechanisms, and change mask constraints to achieve high-accuracy, robust semantic change detection. Notably, the efficacy of these direct classification methods is contingent upon the availability and quality of training samples, which are particularly challenging to obtain for "from-to" variation types, especially in the context of dataintensive Convolutional Neural Networks (CNNs) [33].…”

Section: > Tgrs-2023-04757r1mentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Vector Polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote-sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this paper proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multi-scale extraction, cropping-with-inpainting approach. Next, datasets derived from pre-and post-temporal images are used for training and testing, respectively, and the training set is purified by using twoclassifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs finegrained scene classification. The change rules and postprocessing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on dataset-1, respectively. Similarly, in dataset-2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method's efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

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Section: > Tgrs-2023-04757r1mentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…Ke et al [41] proposed a MCCRNet for change detection. In recent years, multi-task methods [42]- [47] have become popular, that is, to perform semantic segmentation and CD simultaneously on bitemporal remote sensing images, and the segmentation and CD can optimize and improve each other. Metric learning is also a commonly used method in CD tasks.…”

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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“…Frameworks based on SS can be further divided into two types: (1) the post-classification change detection (PCCD) method, as shown in Figure 1a, which judges the existence of change areas by comparing the SS results of dual-temporal images. For example, Xia et al [15] proposed a deep Siamese fusion post-classification network, and Peng et al [40] proposed a SCD method based on the Siamese U-Net network, which further introduced metric learning and deep supervision strategies to improve the network performance. The drawbacks of this method are that its accuracy mainly depends on the accuracy of the predicted land cover map, and that the prediction error will accumulate gradually.…”

Section: Semantic Change Detectionmentioning

confidence: 99%

“…Deep learning-based approaches in SCD have achieved good performance, including independent three-branch networks (which respectively handle semantic segmentation (SS) and BCD) [14], post-classification change detection methods [15], and direct classification methods, etc. However, these methods commonly ignore the inherent relationship between the two sub-tasks and encounter challenges in effectively acquiring temporal features, which limits the precision of SCD.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Semantic Perception Network for Remote Sensing Image Semantic Change Detection

He,

Zhang,

Ning

et al. 2023

Remote Sensing

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Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed “from-to” change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively.

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A Deep Siamese Postclassification Fusion Network for Semantic Change Detection

Cited by 36 publications

References 60 publications

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

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

Spatial-Temporal Semantic Perception Network for Remote Sensing Image Semantic Change Detection

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