Semantic Segmentation and Spatial Relationship Modeling in Hyperspectral Imagery Using Deep Learning and Graph-Based Representations

The effective analysis of spatial data from diverse sources, such as satellite imagery and aerial views, remains pivotal for informed decision-making across various domains. This paper presents a novel framework for semantic segmentation and spatial relationship modeling in hyperspectral imagery using advanced deep learning techniques and graph-based representations. Our methodology integrates state-of-the-art models including U-Net, SegNet, and ResUNet to achieve precise object segmentation in high-resolution images, leveraging their capabilities in capturing fine details and intricate boundaries. By preprocessing data from datasets like the Dubai dataset for semantic segmentation and drone datasets for spatial analysis, we standardize and enhance image quality, preparing them for robust model training. Following segmentation, we employ a relation network model based on LSTM and GRU architectures to extract semantic relationships between identified object pairs within the segmented images. This approach draws on principles from scene graph generation, enhancing scene understanding by structuring spatial relationships into interpretable graph representations. The framework's versatility is demonstrated through applications in urban planning, environmental monitoring, and disaster response, where the visualization of spatial interactions as graphs facilitates intuitive decision-making processes. Through rigorous evaluation and comparison with benchmark methods, we validate the effectiveness of our approach in accurately capturing spatial relationships and deriving actionable insights from complex satellite imagery datasets.