Summary of Lcd-net: a Lightweight Remote Sensing Change Detection Network Combining Feature Fusion and Gating Mechanism, by Wenyu Liu et al.
LCD-Net: A Lightweight Remote Sensing Change Detection Network Combining Feature Fusion and Gating Mechanism
by Wenyu Liu, Jindong Li, Haoji Wang, Run Tan, Yali Fu, Qichuan Tian
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Lightweight remote sensing Change Detection Network (LCD-Net) addresses the limitations of traditional CNN-based methods in remote sensing image change detection. By employing MobileNetV2 as an encoder, LCD-Net efficiently extracts features from bitemporal images while maintaining high detection performance. The Temporal Interaction and Fusion Module (TIF), Feature Fusion Module (FFM), and Gated Mechanism Module (GMM) enhance feature learning by capturing temporal context awareness, aggregating multiscale features, and dynamically adjusting channel weights to emphasize key change regions. Experiments on LEVIR-CD+, SYSU, and S2Looking datasets demonstrate competitive performance with just 2.56M parameters and 4.45G FLOPs, making LCD-Net suitable for real-time applications in resource-limited settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LCD-Net is a new way to detect changes in pictures taken from far away. It’s important for monitoring the environment and helping after natural disasters. The old method used was good but too big and slow for some devices. This new method is smaller and faster while still being very accurate. It uses a special kind of AI called MobileNetV2 to look at two pictures taken at different times. Then it uses three special modules to find the changes: one helps understand the changes over time, another combines small details into big patterns, and the last one makes sure we only see what’s really important. This new method worked well on some big datasets and is perfect for using in places with limited computer power. |
Keywords
» Artificial intelligence » Cnn » Encoder
