Adaptive Batch Extraction For Hyperspectral Image Classification Based On Convolutional Neural Network

Deep Learning for Hyperspectral Imaging Classification is a wonderful solution, despite a few fuzzification. Conventional neural networks are very effective for classification tasks which have allowed them to be used by a very large companies. In this paper, we present an approach to initialize the convolutional data: Firstly, an adaptive selection of kernels by a clustering algorithm; Secondly, by the definition of adaptive batches size. In order to validate our proposed approach, we tested the algorithms on three different hyperspectral images, and the results showed the effectiveness of our proposal.