Detection and Classification of Satellite Remote Sensing Images Using Hybrid Segmentation and Feature Extraction with Effective Algorithms

The remote sensing image analysis, classification, and pattern recognition processes all depend on image segmentation. In this research, a search-based convolutional neural network (SBCNN) is used to identification method for remote sensing images. Prior to applying the image data to the SKFCM with PeSOA segmentation step, the image data must first undergo pre-processing. When pre-processing satellite images for road networks, noise is removed using an improved median filtering technique. The image is then segmented using the SKFCM with PeSOA Segmentation technique to have inverse shape determination with lowest energy usage. Using an intensity constraint, it is possible to identify the segments of a building and vegetation, a road, and a barren area of land. Following segmentation, MLBP with DWT feature extraction is performed on the road satellite images, and the SBCNN is then used to categorize the images. After associated with obtainable methods, the findings of the suggested technique display excellent precision of 98.6%.