Fusion of Optical and SAR Images for Deep-Learning-Based Change Detection

A dependable system that combines several imaging approaches is required because traditional CD methods are inaccurate because of issues including cloud cover, low light levels, and inconsistent data sources. To overcome these constraints, our study combines deep learning methods with optical imaging and SAR. While optical imagery offers high-resolution details, SAR imaging guarantees data capture in weather and illumination circumstances. Our model efficiently combines GAN and CNN data sources for accurate and real-time landscape change detection by utilizing a deep learning framework. This system, which tracks urbanization, wetland loss, and deforestation, is essential for environmental monitoring, disaster management, and infrastructure development. It also supports agriculture through effective crop health monitoring, helps emergency response teams assess the effects of disasters, and improves security applications like border surveillance and unauthorized activity detection, offering a strong answer to pressing societal issues.