Spatial data mining-focused research on image processing techniques for remotely sensed images

Abstract

ABSTRACT Spatial data mining is an important approach for collecting useful data from big datasets, especially remotely sensed images. This study tackles issues in environmental monitoring and management using sophisticated image processing. The Horse Herd Optimization-based VGG19 (HHO-VGG19) is proposed to improve land cover classification, recognition of objects, detection of changes, and detection of anomalies. The study used the BCDD dataset, which was scaled to 512 × 512 pixels, then applied Z-score normalization and extracted features using Principal Component Analysis (PCA). The VGG19 architecture was enhanced by utilizing Horse Herd Optimization to enhance image classification efficiency. The HHO-VGG19 model surpasses conventional techniques, with F1-score of 92%, a recall of 94%, an accuracy of 98.5%, and a 30-second execution time reduction. The findings indicate the efficiency of integrating sophisticated image processing with spatial data mining, giving an effective tool for remote sensing image processing in environmental uses including tracking ecosystems and handling of natural resources.