Approaches for Hyperspectral Image Classification Detailed Review

Abstract

Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.

A review of machine learning methods applied to structural dynamics and vibroacoustic

“…Back in the 1990s, many NN applications in active control had already been identified with three usual configurations [138][139][140]: NN-based model predictive control, in which an NN black-box models the forward dynamics of the system [141]; as an NN-based model-free controller [142]; and in NN-based model reference control, where NN models the plant and optimizes the controller parameters [143]. The first and third configurations use NN in the system modeling stage, while the second and third configurations use NN to learn the optimal controller design.…”

“…Vidya and Dharmana [152] implemented a model reference control of a vehicle suspension using an NN reference algorithm and an RNN controller, claiming that it leads to better adaptivity and stability. The drawback of NN-based reference control is that it uses dynamic backpropagation in the optimization, which is computationally expensive [138].…”