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Hyperspectral imaging produces detailed spectral data, allowing precise object segmentation. This study utilizes a U-Net architecture for segmentation followed by lacunarity analysis of the Indian Pines dataset. The U-Net model, comprising encoder and decoder paths, performs semantic segmentation. Lacunarity, a measure of spatial heterogeneity, is computed using the variance method. Four regions of interest (ROIs) are studied using a gliding box technique. The results demonstrate that integrating segmentation and lacunarity analysis provides nuanced insights into spatial patterns, enhancing interpretation of terrain complexity.
Recent advancements in sensor technologies have enabled hyperspectral satellite imaging (HSI) to capture extensive spectral information, revolutionizing various industries such as precision farming and surveillance. Deep learning techniques, including convolutional neural networks (CNNs), have played a significant role in hyperspectral image analysis. However, validating HSI segmentation algorithms remains challenging due to the lack of annotated ground-truth sets. The Indian Pines dataset is frequently used for testing segmentation techniques. This dataset, containing 145 spectral bands, covers a diverse range of land cover types, making it ideal for analysis.
The Indian Pines dataset comprises 16 land cover classes and 145 spectral bands, covering an area in Northwestern Indiana. This dataset includes farmland, natural land cover classes, and areas covered by various tree species, making it suitable for comprehensive analysis and classification of land cover patterns.
Recent advancements in U-Net-based networks have improved segmentation accuracy and speed. This study employs a U-Net model with specific parameters for training and testing. Lacunarity analysis, performed using a gliding box technique, enhances feature characterization in hyperspectral images. The variance method is used to compute lacunarity, providing insights into spatial heterogeneity.
Segmentation of the Indian Pines image enables focused lacunarity analysis, revealing spatial patterns and texture differences among land cover classes. The integrated approach enhances understanding of landscape heterogeneity and facilitates applications such as environmental monitoring and land-use planning.
The combination of segmentation and lacunarity analysis offers a comprehensive understanding of hyperspectral images. This integrated approach improves feature extraction accuracy and supports decision-making in various fields. By leveraging advanced techniques like U-Net and lacunarity analysis, researchers can unlock valuable insights from hyperspectral data.