Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets
The precise classification of land covers with hyperspectral imagery (HSI) is a major
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …
research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) …
SpaSSA: Superpixelwise adaptive SSA for unsupervised spatial–spectral feature extraction in hyperspectral image
Singular spectral analysis (SSA) has recently been successfully applied to feature extraction
in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D …
in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D …
Novel two-dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging
Feature extraction is of high importance for effective data classification in hyperspectral
imaging (HSI). Considering the high correlation among band images, spectral-domain …
imaging (HSI). Considering the high correlation among band images, spectral-domain …
Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis
Hyperspectral imaging (HSI) classification has become a popular research topic in recent
years, and effective feature extraction is an important step before the classification task …
years, and effective feature extraction is an important step before the classification task …
Folded LDA: extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing
The rich spectral information provided by hyperspectral imaging has made this technology
very useful in the classification of remotely sensed data. However, classification of …
very useful in the classification of remotely sensed data. However, classification of …
Singular spectrum analysis for effective feature extraction in hyperspectral imaging
As a very recent technique for time-series analysis, singular spectrum analysis (SSA) has
been applied in many diverse areas, where an original 1-D signal can be decomposed into …
been applied in many diverse areas, where an original 1-D signal can be decomposed into …
Advancing the interpretation of shallow water marine soundscapes
Soundscapes offer rich descriptions of composite acoustic environments. Characterizing
marine soundscapes simply through sound levels results in incomplete descriptions, limits …
marine soundscapes simply through sound levels results in incomplete descriptions, limits …
Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning
Quantifying phenolic compound in peated barley malt and discriminating its origin are
essential to maintain the aroma of high-quality Scottish whisky during the manufacturing …
essential to maintain the aroma of high-quality Scottish whisky during the manufacturing …
Joint bilateral filtering and spectral similarity-based sparse representation: A generic framework for effective feature extraction and data classification in hyperspectral …
Classification of hyperspectral images (HSI) has been a challenging problem under active
investigation for years especially due to the extremely high data dimensionality and limited …
investigation for years especially due to the extremely high data dimensionality and limited …
[PDF][PDF] Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing.
Hyperspectral imaging is gaining a significant role in agricultural remote sensing
applications. Its data unit is the hyperspectral cube which holds spatial information in two …
applications. Its data unit is the hyperspectral cube which holds spatial information in two …