A review of deep learning used in the hyperspectral image analysis for agriculture
C Wang, B Liu, L Liu, Y Zhu, J Hou, P Liu… - Artificial Intelligence …, 2021 - Springer
Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can
capture up to several hundred images of different wavelengths and offer relevant spectral …
capture up to several hundred images of different wavelengths and offer relevant spectral …
Residual spectral–spatial attention network for hyperspectral image classification
In the last five years, deep learning has been introduced to tackle the hyperspectral image
(HSI) classification and demonstrated good performance. In particular, the convolutional …
(HSI) classification and demonstrated good performance. In particular, the convolutional …
Hyperspectral unmixing via total variation regularized nonnegative tensor factorization
Hyperspectral unmixing decomposes a hyperspectral imagery (HSI) into a number of
constituent materials and associated proportions. Recently, nonnegative tensor factorization …
constituent materials and associated proportions. Recently, nonnegative tensor factorization …
Achieving better category separability for hyperspectral image classification: A spatial–spectral approach
The task of hyperspectral image (HSI) classification has attracted extensive attention. The
rich spectral information in HSIs not only provides more detailed information but also brings …
rich spectral information in HSIs not only provides more detailed information but also brings …
Hyperspectral image classification based on superpixel feature subdivision and adaptive graph structure
The graph-based hyperspectral image classification (HSIC) method has attracted wide
attention because it can extract information with a non-Euclidean structure. Many graph …
attention because it can extract information with a non-Euclidean structure. Many graph …
Aerial scene parsing: From tile-level scene classification to pixel-wise semantic labeling
Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure
of the image content, eg, by assigning a semantic label to every pixel of the image. With the …
of the image content, eg, by assigning a semantic label to every pixel of the image. With the …
Tensor alignment based domain adaptation for hyperspectral image classification
Y Qin, L Bruzzone, B Li - IEEE Transactions on Geoscience and …, 2019 - ieeexplore.ieee.org
This paper presents a tensor alignment (TA) based domain adaptation (DA) method for
hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first …
hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first …
Superpixel tensor model for spatial–spectral classification of remote sensing images
Nowadays, many methods of spatial-spectral classification have been developed and
achieved good results for classification with high-resolution remotely sensed images …
achieved good results for classification with high-resolution remotely sensed images …
Low-rank tensor learning for classification of hyperspectral image with limited labeled samples
Previous studies have demonstrated that integrating spatial information can potentially
provide significant improvements for classification of hyperspectral image (HSI). However, it …
provide significant improvements for classification of hyperspectral image (HSI). However, it …
Hierarchical multi-view semi-supervised learning for very high-resolution remote sensing image classification
Traditional classification methods used for very high-resolution (VHR) remote sensing
images require a large number of labeled samples to obtain higher classification accuracy …
images require a large number of labeled samples to obtain higher classification accuracy …