Stochastic Perturbations on Low-Rank Hyperspectral Data for Image Classification

International Journal of artificial intelligence research

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Title Stochastic Perturbations on Low-Rank Hyperspectral Data for Image Classification
Creator Sumarsono, Alex
Ganjeizadeh, Farnaz
Tomasi, Ryan
Subject Artificial Intelligence; Machine Learning; Image Processing; Signal Processing;
Hyperspectral Imagery; Hyperspectral Image Processing; Low Rank and Sparse Decomposition; Stochastic Perturbations;
Description Hyperspectral imagery (HSI) contains hundreds of narrow contiguous bands of spectral signals. These signals, which form spectral signatures, provide a wealth of information that can be used to characterize material substances. In recent years machine learning has been used extensively to classify HSI data. While many excellent HSI classifiers have been proposed and deployed, the focus has been more on the design of the algorithms. This paper presents a novel data preprocessing method (LRSP) to improve classification accuracy by applying stochastic perturbations to the low-rank constituent of the dataset. The proposed architecture is composed of a low-rank and sparse decomposition, a degradation function and a constraint least squares filter. Experimental results confirm that popular state-of-the-art HSI classifiers can produce better classification results if supplied by LRSP-altered datasets rather than the original HSI datasets. 
Publisher STMIK Dharma Wacana
Contributor California State University, East Bay
Date 2021-01-05
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source International Journal of Artificial Intelligence Research; Vol 5, No 1 (2021): Articles in press; 1 - 12
Language eng
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research

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