Summary of Treat Stillness with Movement: Remote Sensing Change Detection Via Coarse-grained Temporal Foregrounds Mining, by Xixi Wang et al.
Treat Stillness with Movement: Remote Sensing Change Detection via Coarse-grained Temporal Foregrounds Mining
by Xixi Wang, Zitian Wang, Jingtao Jiang, Lan Chen, Xiao Wang, Bo Jiang
First submitted to arxiv on: 15 Aug 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 Coarse-grained Temporal Mining Augmented (CTMA) framework combines bi-temporal images and motion cues for remote sensing image change detection. It transforms bi-temporal images into a video, extracts motion features using temporal encoders, and integrates global and local information with coarse-grained Foregrounds Augmented Spatial Encoder modules. The framework also incorporates ResNet features, spatial blocks for fine-grained feature learning, and mask augmented strategy for improved predictions. Experimental results on multiple benchmark datasets validate the effectiveness of CTMA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have two pictures taken from the same place at different times. You want to figure out what changed between those two moments. This paper shows a new way to do that using videos created from these pictures and special computer programs that can learn from them. It’s like using a superpower to spot changes in your favorite hiking trail! |
Keywords
» Artificial intelligence » Encoder » Mask » Resnet
