Reconstruction-Assisted Band Selection for Non-Destructive Prediction of Citrus Soluble Solids Content from VNIR Hyperspectral Images [PDF]
FoodsThe increasing demand for better fruit flavor and eating quality has driven the need for rapid and non-destructive assessment of internal attributes to support fruit grading and precision supply.
Junjie Zhao +6 more
doaj +2 more sources
An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor
Remote Sensing, 2019 A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis.
Qiang Li, Qi Wang, Xuelong Li
doaj +3 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
Illumination invariance and shadow compensation on hyperspectral images [PDF]
, 2014 To obtain intrinsic reflectance of the scene by hyperspectral imaging systems has been a scientific and engineering challenge. Factors such as illumination variations, atmospheric effects and viewing geometries are common artefacts which modulate the way
Ibrahim, Izzati
core +7 more sources
EBARec-BS: Effective Band Attention Reconstruction Network for Hyperspectral Imagery Band Selection
Remote Sensing, 2021 Hyperspectral band selection (BS) is an effective means to avoid the Hughes phenomenon and heavy computational burden in hyperspectral image processing.
Yufei Liu +3 more
doaj +1 more source
Problem-based band selection for hyperspectral images [PDF]
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017 This paper addresses the band selection of a hyperspectral image. Considering a binary classification, we devise a method to choose the more discriminating bands for the separation of the two classes involved, by using a simple algorithm: single-layer neural network. After that, the most discriminative bands are selected, and the resulting reduced data
Habermann, Mateus +2 more
openaire +3 more sources
Improved SR-SSIM Band Selection Method Based on Band Subspace Partition
Remote Sensing, 2023 Scholars have performed much research on reducing the redundancy of hyperspectral data. As a measure of the similarity between hyperspectral bands, structural similarity is used in band selection methods.
Tingrui Hu +3 more
doaj +1 more source
A Split-and-Merge Approach for Hyperspectral Band Selection [PDF]
IEEE Geoscience and Remote Sensing Letters, 2017 The problem of band selection (BS) is of great importance to handle the curse of dimensionality for hyperspectral image (HSI) applications (e.g., classification). This letter proposes an unsupervised BS approach based on a split-and-merge concept. This new approach provides relevant spectral sub-bands by splitting the adjacent bands without violating ...
Shaheera Rashwan, Nicolas Dobigeon
openaire +4 more sources
Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection
Remote Sensing, 2020 Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated.
Peifeng Su +2 more
doaj +1 more source