Remote Sensing Image Change Detection Based on Multi-Level Diversity Feature Fusion

Urban topographic changes have a crucial impact on the environment, which serves as the foundation for human survival. However, due to the different spectral response characteristics of various remote sensing satellite sensors and the presence of aging issues, the captured remote sensing images exhibit color imbalance under different seasons and lighting conditions. Existing change detection methods fail to establish the correlation between changed and unchanged information in color-imbalanced urban topographic remote sensing images, thereby reducing the utilization of feature extraction information. Additionally, using fixed thresholds for region determination may inaccurately and incompletely generate change areas. Therefore, this paper proposes a remote sensing image change detection method based on multi-level and multi-diversity feature fusion. Firstly, a ResNet-18 network is employed to extract dual-temporal features. Secondly, to enhance the discrimination between change and unchanged regions during feature extraction and improve the utilization of feature extraction information, a Diversity Feature Fusion Module (DFFM) is designed to reduce false alarms and omissions in the detection results. Furthermore, to effectively address the problem of boundary misjudgment in change areas caused by fixed thresholds, an Adaptive Threshold Module is devised to adaptively learn the texture features of change and unchanged regions, enabling the generation of more accurate thresholds for boundary determination, thereby improving the robustness of the algorithm model and alleviating false alarms. Finally, experimental tests demonstrate that our method achieves excellent performance in two public change detection datasets.