The classification of land cover objects in hyperspectral imagery (HSI) has significantly advanced due to the development of convolutional neural networks (CNNs). However. challenges such as limited training data and high dimensionality negatively impact classification performance. Traditional CNN-based methods predominantly utilize 2D CNNs for feature extraction. https://safeersappliancers.shop/product-category/cda-evpk90ss-linear-90cm-island-cooker-hood-stainless-steel/
Enhancing land cover object classification in hyperspectral imagery through an efficient spectral-spatial feature learning approach.
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