Automatic Feature Extraction from High-Resolution Satellite Imagery using Deep Learning techniques

Automated feature extraction from high-resolution satellite data has great potential to locate lucrative resources on Earth. The main elements that are being extracted include Land, Buildings, Roads, Water bodies, and all other necessary elements seen in high-resolution satellite photography. The input dataset includes high-resolution satellite images of New York together with matching map pictures of buildings, roads, landscapes, and water bodies, all in common file formats like.jpg. This paper introduces an innovative feature extraction approach utilizing Deep learning techniques such as Pix2Pix GAN (Generative Adversarial Network) for semantic segmentation and improving the object delineation, such as land, buildings, roads, and water features, in image-to-image translation tasks. U-Net enables precise semantic segmentation by retaining spatial information. Simple CNN (Convolutional Neural Netwok) processes image patches sequentially, extracting relevant features such as edges, textures, and patterns, enabling accurate classification and segmentation of objects in satellite imagery. Pix2Pix GAN is shown to perform better than other models based on Structural Similarity Index Measure (SSIM), a metric used to quantify the similarity between two pictures. It achieved the highest SSIM score of 0.904.