Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …
provided end users with rich spectral, spatial, and temporal information. They have made a …
Toward integrated large-scale environmental monitoring using WSN/UAV/Crowdsensing: A review of applications, signal processing, and future perspectives
A Fascista - Sensors, 2022 - mdpi.com
Fighting Earth's degradation and safeguarding the environment are subjects of topical
interest and sources of hot debate in today's society. According to the United Nations, there …
interest and sources of hot debate in today's society. According to the United Nations, there …
Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants
Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign
particles such as microbial, chemical, and physical contamination in food. This review …
particles such as microbial, chemical, and physical contamination in food. This review …
PCA-based edge-preserving features for hyperspectral image classification
Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to
hyperspectral images (HSIs) have been found very effective in characterizing significant …
hyperspectral images (HSIs) have been found very effective in characterizing significant …
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …
Fusion of dual spatial information for hyperspectral image classification
The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral
imagery has led to significant improvements in terms of classification performance. The task …
imagery has led to significant improvements in terms of classification performance. The task …
Dimensionality reduction and feature selection for object-based land cover classification based on Sentinel-1 and Sentinel-2 time series using Google Earth Engine
Mapping Earth's surface and its rapid changes with remotely sensed data is a crucial task to
understand the impact of an increasingly urban world population on the environment …
understand the impact of an increasingly urban world population on the environment …
Supervised machine learning methods and hyperspectral imaging techniques jointly applied for brain cancer classification
G Urbanos, A Martín, G Vázquez, M Villanueva, M Villa… - Sensors, 2021 - mdpi.com
Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-
ionizing as well as non-invasive. As a consequence, they have been extensively applied in …
ionizing as well as non-invasive. As a consequence, they have been extensively applied in …
Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing
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 …
Analysis (PCA) suffers from high computational cost, large memory requirement and low …
Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold …
In this study, we proposed an automated lithological mapping approach by using spectral
enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible …
enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible …