Spectral and spatial features-based HSI classification using multiple neuron-based learning approach

摘要

With the improvement of remote sensing application, hyperspectral images are used in many applications. Fusion of spatial and spectral data is an actual way in improving the accuracy of hyper-spectral image classification. In this work, we proposed spectral data with spatial details based on hyper-spectral image classification method using neural network classifiers and multi neurons-based learning approach. This is used to classify the remote sensing images with specific class labels. The features may be supernatural and latitudinal data is extracted using boundary values using decision boundary feature extraction (DBFE); which are trained using convolutional neural networks (CNN) to improve the accuracy in labelling the classes. The methodology entails of training with adding regulariser towards the loss function recycled, train the neural networks.