Approaches for Hyperspectral Image Classification Detailed Review
Kushalatha M R1, Prasantha H S2, Beena R. Shetty3
1Kushalatha M R*, Assistant Professor, Department of Electronics and communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India.
2Prasantha H S, Professor, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology (Affiliated to VTU, Belgaum), Bangalore, India.
3Beena R. Shetty, Assistant Professor, Department of Electronics and Communication in Nitte Meenakshi Institute of Technology, Bangalore, India.
Manuscript received on July 29, 2021. | Revised Manuscript received on August 13, 2021. | Manuscript published on September 30, 2021.| PP: 13-22 | Volume-11 Issue-1, September 2021. | Retrieval Number: 100.1/ijsce.A35220911121 | DOI: 10.35940/ijsce.A3522.0911121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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: Hyperspectral imaging (HSI), Supervised and Unsupervised Classification, Semi – Supervised Classification, Neural Network, Classification accuracy