Computational Intelligence in Remote Sensing

<p>With the rapid advancement of Earth observation technologies the availability of high-resolution remote sensing (RS) data has expanded across a wide range of domains including environmental monitoring land use classification disaster management and defense. However the processing and interpretation of such data are increasingly challenged by large volumes complex spatial-spectral structures and limited labeled samples. Computational Intelligence (CI) inspired by natural and biological systems offers promising strategies to address these complexities through adaptive learning feature extraction and decision-making.</p><p>This reprint explores the latest CI-driven methodologies applied to remote sensing tasks as reflected in recent advancements such as hybrid retrieval systems lightweight segmentation networks few-shot classification models semantic-enhanced image captioning and dual-domain transformers for change detection. It highlights contributions from contemporary studies that tackle practical challenges in RS including pan-sharpening road extraction radar imaging and hyperspectral unmixing. By integrating theory with state-of-the-art applications the reprint serves as a valuable reference for researchers and professionals working at the intersection of artificial intelligence and Earth observation.</p>