U-Net Segmentation and Lacunarity for Enhanced Analysis of Hyperspectral Imagery

This study explores hyperspectral image segmentation using a U-Net architecture on the Indian Pines dataset, coupled with lacunarity analysis for quantifying spatial heterogeneity. Segmenting the image reveals intricate spatial patterns, influenced by box size, refining terrain interpretation with implications for remote sensing, land use, and environmental monitoring. The integration emphasizes segmentation’s importance for revealing spatial variability, adding depth to lacunarity analysis. Lacunarity extends beyond spectral signatures, facilitating texture analysis and quantifying spatial heterogeneity, crucial for tasks like land cover classification and object detection. Its sensitivity to spatial scale enables exploration of multiscale patterns, enhancing understanding of spatial structures. This comprehensive approach improves insights into landscape texture and spatial patterns, beneficial for precision agriculture and environmental monitoring.