Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection

• URL: http://arxiv.org/abs/2408.04144v1
• Date: Thu, 8 Aug 2024 01:07:28 GMT
• Title: Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection
• Authors: Yi Liu, Chenhao Sun, Hao Ye, Xiangying Liu, Weilong Ju,
• Abstract summary: We introduce the InPhea model, which integrates phenological features into a remote sensing image CD framework. 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.
• Score: 5.109855690325439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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