Recent advances on spectral–spatial hyperspectral image classification: An overview and new guidelines
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …
last four decades from being a sparse research tool into a commodity product available to a …
Unsupervised semantic segmentation by distilling feature correspondences
Unsupervised semantic segmentation aims to discover and localize semantically meaningful
categories within image corpora without any form of annotation. To solve this task …
categories within image corpora without any form of annotation. To solve this task …
From model-based optimization algorithms to deep learning models for clustering hyperspectral images
Hyperspectral images (HSIs), captured by different Earth observation airborne and space-
borne systems, provide rich spectral information in hundreds of bands, enabling far better …
borne systems, provide rich spectral information in hundreds of bands, enabling far better …
KNN-based representation of superpixels for hyperspectral image classification
Superpixel segmentation has been demonstrated to be a powerful tool in hyperspectral
image (HSI) classification. Each superpixel region can be regarded as a homogeneous …
image (HSI) classification. Each superpixel region can be regarded as a homogeneous …
Multiple feature-based superpixel-level decision fusion for hyperspectral and LiDAR data classification
The rapid increase in the number of remote sensing sensors makes it possible to develop
multisource feature extraction and fusion techniques to improve the classification accuracy …
multisource feature extraction and fusion techniques to improve the classification accuracy …
Hyperspectral band selection for lithologic discrimination and geological mapping
Y Tan, L Lu, L Bruzzone, R Guan… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Classification techniques applied to hyperspectral images are very useful for lithologic
discrimination and geological mapping. Classifiers are often applied either to all spectral …
discrimination and geological mapping. Classifiers are often applied either to all spectral …
Bayesian adaptive superpixel segmentation
Superpixels provide a useful intermediate image representation. Existing superpixel
methods, however, suffer from at least some of the following drawbacks: 1) topology is …
methods, however, suffer from at least some of the following drawbacks: 1) topology is …
[HTML][HTML] Combining vector and raster data in regionalization: A unified framework for delineating spatial unit boundaries for socio-environmental systems analyses
Regionalization has emerged as a crucial research area for the past 50 years, including
aggregating smaller areas into larger, contiguous, and/or homogeneous regions. Spatial …
aggregating smaller areas into larger, contiguous, and/or homogeneous regions. Spatial …
[HTML][HTML] Extended SLIC superpixels algorithm for applications to non-imagery geospatial rasters
J Nowosad, TF Stepinski - … Journal of Applied Earth Observation and …, 2022 - Elsevier
Converting an image to a set of superpixels is a useful preprocessing step in many computer
vision applications; it reduces the dimensionality of the data and removes noise. The most …
vision applications; it reduces the dimensionality of the data and removes noise. The most …
Feature extraction via joint adaptive structure density for hyperspectral imagery classification
Feature extraction is known to be an effective way in both reducing computational
complexity and increasing accuracy in hyperspectral imagery (HSI) classification. In this …
complexity and increasing accuracy in hyperspectral imagery (HSI) classification. In this …