Remote Sensing Image Classification

Remote sensing image classification aims to automatically categorize features within satellite or aerial imagery, enabling efficient analysis of large datasets for applications like urban planning and environmental monitoring. Current research emphasizes improving classification accuracy and efficiency through advanced deep learning architectures, including convolutional neural networks (CNNs) enhanced with attention mechanisms, transformers, and state-space models, as well as exploring multimodal data fusion and federated learning approaches for handling decentralized data. These advancements are crucial for extracting meaningful information from increasingly complex and high-resolution imagery, leading to more accurate and timely insights across various scientific disciplines and practical applications.