<![CDATA[The use of hyperspectral image classification algorithms has garnered increasing interest from the scientific community in recent years, especially in the field of geosciences for pattern recognition applications. In order to extract full spectral-spatial characteristics, this study presents feature extraction with hyperspectral CNN (HSCNet), a unique hierarchical neural network architecture. HSCNet can handle computational complexity issues and capture extensive spectral-spatial information with ease. We use factorized cross entropy (FACE) to address the common problem of class imbalance in both experimental and real-world hyperspectral datasets in order to construct an accurate land cover classification system. FACE makes it easier to reconstruct the loss function, which helps to effectively accomplish the goals that have been expressed. We provide a new framework for hyperspectral image (HSI) classification called FACE, which combines components from HSCNet and FACE. Next, we carry out in-depth studies using two different remote sensing datasets: Botswana (BS) and Indian Pines (IP). We compare the effectiveness of different backbone networks in terms of categorization and compare its classification performance under various loss functions. Comparing our suggested classification system against the state-of-the-art end-to-end deep-learningbased techniques, we find encouraging results]]>
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