Hyperspectral image classification using neural networks with effect of feature optimization on fused convolutional features

Analysis of data and synthesis for hyper spectral imaging (HSI) is a new branch of remotely sensed data and planet surveillance technologies. Classification techniques with the help of deep learning for Land cover have recently been a popular research topic, so these techniques are employed in a variety of applications, including agricultural, environmental analysis, military surveillance, mineral extraction and urban exploration. Despite the fact that Convolutional Neural Networks (CNNs) have attracted a lot of attention and produced impressive results in a range of scene classification tasks, their high computational and storage costs restrict them from being employed in real-time remote sensing applications. Traditional CNN-based techniques, on the other hand, focus on producing scene representation by processing features of original image or from CNN, neglecting the fact that texture pictures or each layer of CNNs convey information that is distinct. To efficiently investigate the characteristics and avoid the above-mentioned drawbacks, a pretrained DNN model-based method is offered. The performance of two different hyperspectral classifiers for land use/land cover classification is compared in this study. The HSI images were classified using Multilayer Perceptron Artificial Neural Networks and Support Vector Machines. The experimental results, which were carried out on the Live Aerial Image Hyperspectral Datasets, revealed that classification accuracy is analyzed with and without feature reduction and it is found that with feature reduction performance of proposed algorithm is improved with significant margin with respective to existing aerial image dataset.