IEEE Geoscience and Remote Sensing Magazine - September 2021 - 72
Land cover
classification for
hyperspectral Earth
observation data
A Review of
Unsupervised Band
Selection Techniques
RAM NARAYAN PATRO, SUBHASHREE SUBUDHI,
PRADYUT KUMAR BISWAL, AND FABIO DELL'ACQUA
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 different wavelengths; the spatial information,
however, remains generally consistent across
bands. Both types of information, spectral and spatial, can
be leveraged to identify and classify objects. Recently, the
use of machine learning (ML) in object classification has
become increasingly widespread. Regardless of the selected
Digital Object Identifier 10.1109/MGRS.2021.3051979
Date of current version: 24 February 2021
72 0274-6638/21©2021IEEE
approach, object-specific spectral and spatial information
is key to discriminating relevant categories. Whereas spatial
information is usually repeated across bands, spectral
information tends to be distributed more unevenly and often
highly so. This poses the issue of removing redundancy,
which is commonly called the band selection (BS) problem
and refers to identifying an optimal subset of bands for further
HSI processing.
Depending on the type of information, BS approaches
can be categorized into spectral, spatial, and hybrid techniques.
Moreover, based on the availability and use of
ground truth (GT) information, these approaches can be
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2021
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IEEE Geoscience and Remote Sensing Magazine - September 2021
Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2021
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