Bitemporal Remote Sensing Image Change Detection Network Based on Siamese-Attention Feedback Architecture

Yin, Hongyang; Ma, Chong; Weng, Liguo; Xia, Min; Lin, Haifeng

doi:10.3390/rs15174186

Cited by 5 publications

(2 citation statements)

References 60 publications

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“…The coarse-to-fine boundary refinement network (CBR-Net) [44] can accurately extract building footprints from remote-sensing imagery. A bitemporal remote-sensing image change detection network based on a Siamese-attention feedback architecture, referred to as SAFNet [45], a global semantic module (GSM) on the encoder network, generates a low-resolution semantic change map to capture the changed objects, a temporal interaction module (TIM) and two auxiliary modules-the change feature extraction module (CFEM) and the feature refinement module (FRM)-to learn the fine boundaries of the changed target. The SAFNet algorithm exhibits state-of-the-art performance.…”

Section: Deep-learning Methods For Change Detection (Cd)mentioning

confidence: 99%

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Li,

Zhou,

Zhang

et al. 2024

Remote Sensing

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Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land.

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Section: Deep-learning Methods For Change Detection (Cd)mentioning

confidence: 99%

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Li,

Zhou,

Zhang

et al. 2024

Remote Sensing

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“…Based on the improved decoder-encoder structure of Unet++_MSOF and SNUNet [42], IFNet uses channel attention and spatial attention to optimize the feature weight distribution in the process of multi-scale skip connections. SAGNet and SAFNet [43] add a bi-temporal interaction layer between the encoding layers to communicate the semantic information of the twin branches. Secondly, there are models combining transformers and self-attention mechanisms, such as STANet, which models spatio-temporal relationships through multi-scale pooling and self-attention mechanisms.…”

Section: Comparative Experiments On Different Datasetsmentioning

confidence: 99%

MFINet: Multi-Scale Feature Interaction Network for Change Detection of High-Resolution Remote Sensing Images

Ren,

Wang,

Xia

et al. 2024

Remote Sensing

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Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing methods have not fully tapped the potential of multi-scale bi-temporal features to interact layer by layer. Therefore, this paper proposes a multi-scale feature interaction network (MFINet). The network realizes the information interaction of multi-temporal images by inserting a bi-temporal feature interaction layer (BFIL) between backbone networks at the same level, guides the attention to focus on the difference region, and suppresses the interference. At the same time, a double temporal feature fusion layer (BFFL) is used at the end of the coding layer to extract subtle difference features. By introducing the transformer decoding layer and improving the recovery effect of the feature size, the ability of the network to accurately capture the details and contour information of the building is further improved. The F1 of our model on the public dataset LEVIR-CD reaches 90.12%, which shows better accuracy and generalization performance than many state-of-the-art change detection models.

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Bi-Temporal to Time Series Data Analysis

Bovolo,

Solano-Correa,

Meshkini

et al. 2024

Reference Module in Earth Systems and Environmental Sciences

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Bitemporal Remote Sensing Image Change Detection Network Based on Siamese-Attention Feedback Architecture

Cited by 5 publications

References 60 publications

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

MFINet: Multi-Scale Feature Interaction Network for Change Detection of High-Resolution Remote Sensing Images

Bi-Temporal to Time Series Data Analysis

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