Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
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 …
Interpretable hyperspectral artificial intelligence: When nonconvex modeling meets hyperspectral remote sensing
Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …
Spectral enhanced rectangle transformer for hyperspectral image denoising
Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing
the great power of deep learning, existing HSI denoising methods suffer from limitations in …
the great power of deep learning, existing HSI denoising methods suffer from limitations in …
A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening
Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery
processing, in which high-resolution spatial details from panchromatic images are employed …
processing, in which high-resolution spatial details from panchromatic images are employed …
Hyperspectral image denoising employing a spatial–spectral deep residual convolutional neural network
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the
performance of the subsequent HSI interpretation and applications. In this paper, a novel …
performance of the subsequent HSI interpretation and applications. In this paper, a novel …
Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach
Fusing satellite observations and station measurements to estimate ground‐level PM2. 5 is
promising for monitoring PM2. 5 pollution. A geo‐intelligent approach, which incorporates …
promising for monitoring PM2. 5 pollution. A geo‐intelligent approach, which incorporates …
Hyperspectral image restoration via total variation regularized low-rank tensor decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …
during the acquisition process, eg, Gaussian noise, impulse noise, dead lines, stripes, etc …
Image super-resolution: The techniques, applications, and future
Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …
the observed LR images. As SR has been developed for more than three decades, both …
Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration
In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal
method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In …
method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In …