Research on Change Information Retrieval Method for Energy Infrastructure Based on Optical Remote Sensing Images

With the rapid advancement of satellite remote sensing technology, high-resolution optical remote sensing images have become increasingly crucial for monitoring energy infrastructure. Traditional change detection methods identify land-use and land cover variations over time, while image retrieval extracts pertinent information from large datasets. By integrating both approaches, change information retrieval allows for identifying images with features similar to those in a query time-series from vast archives of temporal data. Research in this direction remains relatively limited at present. To address this, we propose a novel framework that integrates change detection with image retrieval, focusing specifically on the change information in bi-temporal remote sensing images. Our model, SCanNet-CDH (Semantic Change Network and Convolutional Deep Hashing), integrates convolutional neural networks with Transformer architecture, utilizing multi-scale features from ResNet-34 and enhancing them with the CSWin Transformer for improved global feature modeling. A deep hashing technique is applied to the extracted features, optimizing retrieval accuracy via contrastive loss. To address the lack of publicly available biphasic datasets in the energy infrastructure domain, we introduce EICIRD (Energy Infrastructure Change Information Retrieval Dataset), featuring multi-class variation annotations for model training and evaluation. Experimental results demonstrate that the proposed model outperforms existing methods in terms of mean Average Precision, showcasing superior performance, practical applicability, and its potential to advance remote sensing image analysis and energy infrastructure monitoring.