FPGA-Based Satellite Vision Systems using Verilog HDL

Abstract

Abstract: It is believed that the most effective way to collect information about the Earth's surface is through high- quality satellite images. Extracting a feature from an image is really difficult because you have to choose the best image segmentation methods and combine many strategies to find the Region in the most effective manner. This study makes recommendations for the classification techniques for objects in the satellite. On high-resolution satellite images, applying image processing methods. The methods used to define region mostly focus on urban, agricultural, and forest regions. There are several methods for extracting these traits. Using a Grey Level Cooccurrence Matrix is the most used method. It is employed to unveil specific characteristics regarding the spatial arrangement of gray levels in the texture image. The Grey Level Co-occurrence Matrix (GLCM) captures statistical details of neighboring pixels in an image, enabling the computation of textural features that enhance the comprehension of visual content. This research presents a VLSI implementation aimed at extracting four texture characteristics from the grey level co-occurrence matrix. Verilog was employed to model the hardware, with MATLAB used for software simulation. The simulation utilized the Verilog HDL language from the XILINX tool, and the implementation was executed on the SPARTAN FPGA board.