Author(s): Eman Abdullah Aldakheel, Mohammed Zakariah
In remote sensing, segmentation and classification of satellite images are a demanding task that attributes various kinds of detecting the targeted regions. The accurate segmentation of the targeted area is considered a crucial portion of seeing several geographical positions and locations. In turn, the classification and segmentation technique enhances the detection rate and accurately recognizes the target regions with reduced execution time. A deep learning-dependent automatic detection, segmentation, and classification of satellite images are conducted in this process, employing artificial intelligence methods. At first, the input image is preprocessed, segmented using semantic-ROI segmentation, and classified using the Multiscale Alex Net CNN classifier method. The semantic-based ROI segmentation stage is employed for extracting the regions. By using a multilinear spectral decomposition-based extraction approach, the spatial information is removed. Then the deep learning-based Multiscale Alex Net classifier effectively classifies the satellite images. The dataset employed are Indian Pines, Pavia, and Salinas. The presented approach performance is estimated and the outcomes attained are evaluated to prove the efficiency of the proposed system.