Geospatial Land Classification Via Advanced Image Processing using CNN

Abstract

High resolution analysis of remote sensing images is pivotal for various classification including land use determination, environmental detection, environmental planning and geospatial object recognition. This paper introduces a robust method for categorizing satellite images into distinct groups, facilitating accurate classification for global geographical areas. It includes image compression, image preprocessing, image segmentation and feature extraction. This innovative approach enables precise identification and understanding of different areas, contributing to optimize resource allocation and improved land management practices in agriculture. CNN is the classifier that is employed in this experiment. The outcome demonstrates that our suggested strategy offers excellent accuracy, outperforming many recently published publications.