Approaches for Hyperspectral Image Classification Detailed Review
- 30 September 2021
- journal article
- Published by Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP in International Journal of Soft Computing and Engineering
- Vol. 11 (1), 13-22
- https://doi.org/10.35940/ijsce.a3522.0911121
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.Keywords
This publication has 29 references indexed in Scilit:
- Identifying urban vegetation stress factors based on open access remote sensing imagery and field observationsEcological Informatics, 2020
- Hyperspectral image unsupervised classification by robust manifold matrix factorizationInformation Sciences, 2019
- Hyperspectral band selection for soybean classification based on information measure in FRS theoryBiosystems Engineering, 2018
- Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural NetworksJournal of the Indian Society of Remote Sensing, 2018
- Hyperspectral image classification using k-sparse denoising autoencoder and spectral–restricted spatial characteristicsApplied Soft Computing, 2018
- Hyperspectral image classification using spectral-spatial LSTMsNeurocomputing, 2018
- Hyperspectral image classification based on adaptive segmentationOptik, 2018
- Beyond RGB: Very high resolution urban remote sensing with multimodal deep networksISPRS Journal of Photogrammetry and Remote Sensing, 2018
- A novel approach for vegetation classification using UAV-based hyperspectral imagingComputers and Electronics in Agriculture, 2018
- Urban Tree Species Mapping Using Airborne LiDAR and Hyperspectral DataJournal of the Indian Society of Remote Sensing, 2016