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

TL;DR

This paper introduces InPhea, a novel model that integrates phenological features and advanced attention mechanisms to improve remote sensing image change detection, effectively distinguishing true land cover changes from phenological pseudo-changes.

Contribution

The paper presents a new framework that incorporates phenological features with a differential attention module and multi-stage contrastive learning for enhanced change detection accuracy.

Findings

InPhea outperforms existing models on multiple datasets.

Effective in reducing phenological pseudo-changes.

Demonstrates superior feature representation and change detection performance.

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.