Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection
<|reference_start|>Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection: Remote sensing image change detection (CD) is essential for analyzing land surface changes over time, with a significant challenge being the differentiation of actual changes from complex scenes while filtering out pseudo-changes. A primary contributor to this challenge is the intra-class dynamic changes due to phenological characteristics in natural areas. To overcome this, we introduce the InPhea model, which integrates phenological features into a remote sensing image CD framework. The model features a detector with a differential attention module for improved feature representation of change information, coupled with high-resolution feature extraction and spatial pyramid blocks to enhance performance. Additionally, a constrainer with four constraint modules and a multi-stage contrastive learning approach is employed to aid in the model's understanding of phenological characteristics. Experiments on the HRSCD, SECD, and PSCD-Wuhan datasets reveal that InPhea outperforms other models, confirming its effectiveness in addressing phenological pseudo-changes and its overall model superiority.<|reference_end|>
@article{liu2024integrated,
    title={Integrated Dynamic Phenological Feature for Remote Sensing Image Land
  Cover Change Detection},
    author={Yi Liu, Chenhao Sun, Hao Ye, Xiangying Liu and Weilong Ju},
    journal={arXiv preprint arXiv:2408.04144},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04144},
    primaryClass={cs.CV}
}