Classification techniques for hyperspectral remote sensing [PDF]
, 2011 This study concerns with classification techniques in high dimensional space such as that of Hyperspectral Imaging (HSI) data sets, with objectives of understanding the strength and weakness of various classifiers and at the same time to study how ...
Kam, Firmin
core +7 more sources
Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [PDF]
, 2014 As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei +6 more
core +4 more sources
Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery [PDF]
, 2013 With increasing applications of hyperspectral imagery (HSI) in agriculture, mineralogy, military, and other fields, one of the fundamental tasks is accurate detection of the target of interest.
Li, Xiaohui +3 more
core +4 more sources
Singular spectrum analysis for effective feature extraction in hyperspectral imaging [PDF]
, 2014 As a very recent technique for time series analysis, Singular Spectrum Analysis (SSA) has been applied in many diverse areas, where an original 1D signal can be decomposed into a sum of components including varying trends, oscillations and noise ...
Zabalza, Jaime +4 more
core +4 more sources
Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition
Remote Sensing, 2021 Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest.
Marzieh Zare +3 more
doaj +1 more source
How Hyperspectral Image Unmixing and Denoising Can Boost Each Other
Remote Sensing, 2020 Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the
Behnood Rasti +3 more
doaj +1 more source
A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching
Remote Sensing, 2021 Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications.
Yang Yu +5 more
doaj +1 more source
Intraoperative reperfusion assessment of human pancreas allografts using hyperspectral imaging (HSI) [PDF]
Hepatobiliary Surgery and Nutrition, 2022 The most common causes of early graft loss in pancreas transplantation are insufficient blood supply and leakage of the intestinal anastomosis. Therefore, it is critical to monitor graft perfusion and oxygenation during the early post-transplant period. The goal of our pilot study was to evaluate the utility of hyperspectral imaging (HSI) in monitoring
Robert, Sucher +9 more
openaire +2 more sources
Remote Sensing, 2020 Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples.
Yunhao Zou +3 more
doaj +1 more source
A Lightweight Multi-Level Information Network for Multispectral and Hyperspectral Image Fusion
Remote Sensing, 2022 The process of fusing the rich spectral information of a low spatial resolution hyperspectral image (LR-HSI) with the spatial information of a high spatial resolution multispectral image (HR-MSI) to obtain an HSI with the spatial resolution of an MSI ...
Mingming Ma +4 more
doaj +1 more source