Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox
Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …
A review of unsupervised band selection techniques: Land cover classification for hyperspectral earth observation data
A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide
spectral range. Each band reflects the same scene, composed of various objects imaged at …
spectral range. Each band reflects the same scene, composed of various objects imaged at …
Hyperspectral image classification: An analysis employing CNN, LSTM, transformer, and attention mechanism
Hyperspectral images contain tens to hundreds of bands, implying a high spectral
resolution. This high spectral resolution allows for obtaining a precise signature of structures …
resolution. This high spectral resolution allows for obtaining a precise signature of structures …
Hyperspectral image classification via deep structure dictionary learning
The construction of diverse dictionaries for sparse representation of hyperspectral image
(HSI) classification has been a hot topic over the past few years. However, compared with …
(HSI) classification has been a hot topic over the past few years. However, compared with …
[PDF][PDF] A survey of band selection techniques for hyperspectral image classification
SS Sawant, M Prabukumar - Journal of Spectral Imaging, 2020 - pdfs.semanticscholar.org
The hyperspectral imaging technology discussed here captures a scene by using various
imaging spectrometer sensors [eg Airborne Visible Infrared Imaging Spectrometer (AVIRIS) …
imaging spectrometer sensors [eg Airborne Visible Infrared Imaging Spectrometer (AVIRIS) …
Hyperspectral band selection for spectral–spatial anomaly detection
Owing to significantly improved spectral resolution, a hyperspectral imaging sensor can now
uncover many unknown subtle material substances. In many cases, anomalies are usually …
uncover many unknown subtle material substances. In many cases, anomalies are usually …
An Unsupervised Feature Extraction Using Endmember Extraction and Clustering Algorithms for Dimension Reduction of Hyperspectral Images
Hyperspectral images (HSIs) provide rich spectral information, facilitating many applications,
including landcover classification. However, due to the high dimensionality of HSIs …
including landcover classification. However, due to the high dimensionality of HSIs …
Estimation of the total nonstructural carbohydrate concentration in apple trees using hyperspectral imaging
YS Kang, KS Park, ER Kim, JC Jeong, CS Ryu - Horticulturae, 2023 - mdpi.com
The total nonstructural carbohydrate (TNC) concentration is an important indicator of the
growth period and health of fruit trees. Remote sensing can be applied to monitor the TNC …
growth period and health of fruit trees. Remote sensing can be applied to monitor the TNC …
Development of spectral indexes in hyperspectral imagery for land cover assessment
DM Varade, AK Maurya, O Dikshit - IETE Technical Review, 2019 - Taylor & Francis
Spectral indexes (SI) are widely used for land cover characterization and also in several
physical models for the study of land surface processes. For example, the normalized …
physical models for the study of land surface processes. For example, the normalized …
[PDF][PDF] Clustering-Based Band Selection Using Structural Similarity Index and Entropy for Hyperspectral Image Classification.
A Ghorbanian, Y Maghsoudi… - Traitement du …, 2020 - researchgate.net
Accepted: 15 October 2020 Despite the unique capabilities of hyperspectral images for
classification tasks, handling the high dimension of these data is challenging. Therefore …
classification tasks, handling the high dimension of these data is challenging. Therefore …