ATCFNet: A Lightweight Cross-Level Attention-Guided High-Resolution Remote Sensing Image Change Detection Network.
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| Title: | ATCFNet: A Lightweight Cross-Level Attention-Guided High-Resolution Remote Sensing Image Change Detection Network. |
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| Authors: | Li, Dongxu1 (AUTHOR), Chu, Peng1,2 (AUTHOR), Yang, Chen2,3 (AUTHOR), Wang, Zhen1,3,4 (AUTHOR) wzgroup@nwpu.edu.cn, Dai, Chuanjin1,4 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1306. 31p. |
| Subjects: | Remote sensing, Edge computing, Object recognition (Computer vision), Real-time computing, Image analysis, High resolution imaging, Artificial neural networks |
| Abstract: | Highlights: What are the main findings? ATCFNet achieves state-of-the-art accuracy with only 3.71 M parameters and 3.0 G FLOPs, attaining F1 scores of 91.46%, 77.05%, and 83.53% on LEVIR-CD, HRCUS, and SYSU-ChangeDet datasets, respectively. The three-step progressive optimization strategy (AFAM, TACM, and EGM) effectively addresses blurred boundaries, missed small-object detection, and internal voids in changed regions. What are the implications of the main findings? The network provides a deployable solution for real-time change detection on re-source-constrained edge devices (e.g., UAVs and satellite on-board processors) without requiring model compression. The "local detail–cross-layer semantics–global dependency" collaborative framework demonstrates that lightweight designs can achieve comparable or superior performance to heavy models in high-resolution remote sensing applications. Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving real-time accurate change detection on edge computing devices (e.g., drone-embedded chips, satellite on-board processors) has become an urgent challenge—existing deep learning methods, despite high accuracy, are hindered by massive parameters and computational costs that preclude deployment on resource-constrained embedded hardware. To address this, we focus on lightweight (i.e., low parameter count and low computational cost) RSCD network design, targeting three critical bottlenecks: blurred boundaries of changed regions, missed detection of small objects, and insufficient computational efficiency. We propose ATCFNet (Adjacent-Temporal Cross Fusion Network), featuring a three-step progressive feature optimization strategy: (1) the Adjacent Feature Aggregation Module (AFAM) enhances shallow geometric details via lateral three-stage fusion to compensate for lightweight backbones; (2) the Temporal Attention Cross Module (TACM) integrates cross-level feature propagation and Convolutional Block Attention Module (CBAM) for collaborative optimization of high-level semantics and low-level details; and (3) the Efficient Guidance Module (EGM) establishes long-range dependencies using shared change priors and lightweight self-attention to suppress internal voids in changed regions. Experiments on three public datasets (LEVIR-CD, HRCUS, SYSU-ChangeDet) demonstrate that ATCFNet achieves state-of-the-art accuracy with merely 3.71 million (M) parameters and 3.0 billion (G) floating-point operations (FLOPs)—F1-scores of 91.46%, 77.05%, and 83.53%, significantly outperforming 18 existing methods in most indicators. Notably, it excels in edge integrity (avoiding jagged blurring at change boundaries) and small-target detection in high-resolution urban scenes. This study provides an efficient and reliable lightweight solution for edge computing scenarios such as real-time drone inspection and satellite on-board intelligent processing. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? ATCFNet achieves state-of-the-art accuracy with only 3.71 M parameters and 3.0 G FLOPs, attaining F1 scores of 91.46%, 77.05%, and 83.53% on LEVIR-CD, HRCUS, and SYSU-ChangeDet datasets, respectively. The three-step progressive optimization strategy (AFAM, TACM, and EGM) effectively addresses blurred boundaries, missed small-object detection, and internal voids in changed regions. What are the implications of the main findings? The network provides a deployable solution for real-time change detection on re-source-constrained edge devices (e.g., UAVs and satellite on-board processors) without requiring model compression. The "local detail–cross-layer semantics–global dependency" collaborative framework demonstrates that lightweight designs can achieve comparable or superior performance to heavy models in high-resolution remote sensing applications. Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving real-time accurate change detection on edge computing devices (e.g., drone-embedded chips, satellite on-board processors) has become an urgent challenge—existing deep learning methods, despite high accuracy, are hindered by massive parameters and computational costs that preclude deployment on resource-constrained embedded hardware. To address this, we focus on lightweight (i.e., low parameter count and low computational cost) RSCD network design, targeting three critical bottlenecks: blurred boundaries of changed regions, missed detection of small objects, and insufficient computational efficiency. We propose ATCFNet (Adjacent-Temporal Cross Fusion Network), featuring a three-step progressive feature optimization strategy: (1) the Adjacent Feature Aggregation Module (AFAM) enhances shallow geometric details via lateral three-stage fusion to compensate for lightweight backbones; (2) the Temporal Attention Cross Module (TACM) integrates cross-level feature propagation and Convolutional Block Attention Module (CBAM) for collaborative optimization of high-level semantics and low-level details; and (3) the Efficient Guidance Module (EGM) establishes long-range dependencies using shared change priors and lightweight self-attention to suppress internal voids in changed regions. Experiments on three public datasets (LEVIR-CD, HRCUS, SYSU-ChangeDet) demonstrate that ATCFNet achieves state-of-the-art accuracy with merely 3.71 million (M) parameters and 3.0 billion (G) floating-point operations (FLOPs)—F1-scores of 91.46%, 77.05%, and 83.53%, significantly outperforming 18 existing methods in most indicators. Notably, it excels in edge integrity (avoiding jagged blurring at change boundaries) and small-target detection in high-resolution urban scenes. This study provides an efficient and reliable lightweight solution for edge computing scenarios such as real-time drone inspection and satellite on-board intelligent processing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091306 |
