SAR Image Processing: Synthetic Aperture Radar Imaging and MATLAB Implementation

This project focuses on SAR (Synthetic Aperture Radar) image processing and imaging techniques, providing comprehensive MATLAB implementations. The SAR image processing component covers essential workflow steps including preprocessing (data calibration and geometric correction), filtering algorithms (such as Lee and Gamma MAP filters), noise reduction techniques (wavelet-based denoising), image enhancement methods (histogram equalization and contrast stretching), and feature extraction procedures (edge detection and texture analysis). For synthetic aperture radar imaging and processing, the implementation encompasses fundamental imaging algorithms including Range-Doppler algorithm and Chirp Scaling algorithm for image formation, advanced image reconstruction techniques using backprojection methods, automated target detection algorithms employing CFAR (Constant False Alarm Rate) detection, and sophisticated target tracking methodologies with Kalman filtering implementations. The project provides detailed MATLAB code examples that demonstrate practical implementation of these techniques, featuring key functions such as SAR data import routines, azimuth and range compression algorithms, phase correction modules, and image visualization tools. Each code segment includes comprehensive comments explaining algorithmic approaches and parameter configurations, making it suitable for both research studies and practical applications in remote sensing and radar signal processing.