Effect of Legendre–Fenchel denoising and SVD-based dimensionality reduction algorithm on hyperspectral image classification
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computing and Applications"
DOI: 10.1007/s00521-017-3145-y
Abstract: This paper describes the importance of performing preprocessing techniques namely, denoising and dimensionality reduction to the hyperspectral data before classification. The two main problems faced in hyperspectral image processing are noise and higher dimension. Legendre–Fenchel… read more here.
Keywords: technique; reduction; classification; hyperspectral image ... See more keywords
The linear prediction vector quantization for hyperspectral image compression
Sign Up to like & getrecommendations! Published in 2018 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-018-6724-8
Abstract: In this paper, a hyperspectral image compression method is proposed. It is based on spectral clustering, linear prediction and the vector quantization (VQ). Since the hyperspectral image has stronger spectral correlation than spatial correlation, the… read more here.
Keywords: linear prediction; hyperspectral image; prediction vector; prediction ... See more keywords
Sparse representation-based hyperspectral image classification
Sign Up to like & getrecommendations! Published in 2018 at "Signal, Image and Video Processing"
DOI: 10.1007/s11760-018-1249-1
Abstract: This paper presents an approach for hyperspectral image classification using contextual sparse coefficients based on sparse representations. The main idea is that the sparse coefficients obtained through sparse representation-based modelling of the hyperspectral images contain… read more here.
Keywords: image; sparse representation; sparse coefficients; hyperspectral image ... See more keywords
Urban Shadow Detection and Classification Using Hyperspectral Image
Sign Up to like & getrecommendations! Published in 2017 at "Journal of the Indian Society of Remote Sensing"
DOI: 10.1007/s12524-016-0649-3
Abstract: Shadow is an inevitable problem in high-resolution remote sensing images. There are need and significance in extracting information from shadow-covered areas, such as in land-cover mapping. Although the illumination energy of shadow pixels is low,… read more here.
Keywords: classification; shadow pixels; shadow; hyperspectral image ... See more keywords
A scalable approach for the efficient segmentation of hyperspectral images
Sign Up to like & getrecommendations! Published in 2021 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2021.104314
Abstract: Abstract The number of applications of hyperspectral imaging (HSI) is steadily increasing, as technology evolves and cameras become more affordable. However, the volume of data in a hyperspectral image is large (order of Gigabytes) and… read more here.
Keywords: hyperspectral image; methodology; classifier; approach ... See more keywords
Two-dimensional empirical wavelet transform based supervised hyperspectral image classification
Sign Up to like & getrecommendations! Published in 2017 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2017.09.003
Abstract: Abstract Hyperspectral image classification is one of the major field of application for hyperspectral imaging systems. Though hyperspectral data gives accurate results than their multispectral counterparts, they are computationally more complex due to their high… read more here.
Keywords: classification; image; supervised hyperspectral; two dimensional ... See more keywords
Non-destructive discrimination of sesame oils via hyperspectral image analysis
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Food Composition and Analysis"
DOI: 10.1016/j.jfca.2020.103505
Abstract: Abstract Sesame oil is produced from cheap imported raw materials, but its quality differs depending on the imported form. The purpose of this study was to determine the quality characteristics of sesame oil through hyperspectral… read more here.
Keywords: sesame oil; hyperspectral image; analysis; sesame ... See more keywords
Hyperspectral image control of the heat-treatment process of oat flour to model composite bread properties
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Food Engineering"
DOI: 10.1016/j.jfoodeng.2016.07.017
Abstract: Abstract A hyperspectral image analysis was used to characterize heat treatment in oat flour, performed by treating oat flour at 80, 100 and 130 °C for 30 min. Images from both original oat and treated flours were… read more here.
Keywords: hyperspectral image; heat treatment; oat flour; oat ... See more keywords
Hyperspectral image-based multi-feature integration for TVB-N measurement in pork
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Food Engineering"
DOI: 10.1016/j.jfoodeng.2017.09.003
Abstract: Abstract Total volatile basic nitrogen (TVB-N) content is an important index used to evaluate the freshness of pork. In this paper, a strategy for measurement of TVB-N content in pork through hyperspectral imaging (HSI) (400–1000 nm)… read more here.
Keywords: pork; feature; tvb content; hyperspectral image ... See more keywords
Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.11.034
Abstract: Hyperspectral image (HSI) classification is one of the fundamental tasks in HSI analysis. Recently, many approaches have been extensively studied to improve the classification performance, among which integrating the spatial information underlying HSIs is a… read more here.
Keywords: classification; markov random; dimensional discrete; wavelet transform ... See more keywords
Hyperspectral image super-resolution using deep convolutional neural network
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.05.024
Abstract: Abstract Limited by the existed imagery hardware, it is challenging to obtain a hyperspectral image (HSI) with a high spatial resolution. Super-resolution (SR) focuses on the ways to enhance the spatial resolution. HSI SR is… read more here.
Keywords: resolution; neural network; super resolution; convolutional neural ... See more keywords