  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.
