Brain-inspired remote sensing interpretation: A comprehensive survey
Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …
Through research on brain science, the intelligence of remote sensing algorithms can be …
Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: a survey
MA Moharram, DM Sundaram - Environmental Science and Pollution …, 2023 - Springer
Hyperspectral image (HSI) contains hundreds of adjacent spectral bands, which can
effectively differentiate the region of interest. Nevertheless, many irrelevant and highly …
effectively differentiate the region of interest. Nevertheless, many irrelevant and highly …
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) …
Tensor singular spectrum analysis for 3-D feature extraction in hyperspectral images
Due to the cubic structure of a hyperspectral image (HSI), how to characterize its spectral
and spatial properties in 3-D is challenging. Conventional spectral–spatial methods usually …
and spatial properties in 3-D is challenging. Conventional spectral–spatial methods usually …
Spatial and spectral structure preserved self-representation for unsupervised hyperspectral band selection
As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …
hyperspectral band selection aims to select a subset of informative and discriminative bands …
AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification
Convolutional models have provided outstanding performance in the analysis of
hyperspectral images (HSIs). These architectures are carefully designed to extract intricate …
hyperspectral images (HSIs). These architectures are carefully designed to extract intricate …
Hyperspectral imagery reveals large spatial variations of heavy metal content in agricultural soil-A case study of remote-sensing inversion based on Orbita …
The widespread heavy metal contamination in soil induced by extensive human disturbance
has been a global significant issue because of its chronic toxic effects on human health …
has been a global significant issue because of its chronic toxic effects on human health …
[HTML][HTML] The effect of artificial intelligence evolving on hyperspectral imagery with different signal-to-noise ratio, spectral and spatial resolutions
Hyperspectral images are increasingly being used in classification and identification. Data
users prefer hyperspectral imagery with high spatial resolution, finer spectral resolution, and …
users prefer hyperspectral imagery with high spatial resolution, finer spectral resolution, and …
Multi-objective unsupervised band selection method for hyperspectral images classification
X Ou, M Wu, B Tu, G Zhang, W Li - IEEE Transactions on Image …, 2023 - ieeexplore.ieee.org
With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose
bands based on band correlation and information has become more significant, but also …
bands based on band correlation and information has become more significant, but also …
[HTML][HTML] Response of net primary productivity of vegetation to drought: A case study of Qinba Mountainous area, China (2001–2018)
Drought can significantly affect the carbon cycle of ecosystems. The Qinba Mountains region
has a high potential for developing carbon sink forestry due to its strong carbon fixation …
has a high potential for developing carbon sink forestry due to its strong carbon fixation …