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 …
Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions
MA Moharram, DM Sundaram - Neurocomputing, 2023 - Elsevier
Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …
More diverse means better: Multimodal deep learning meets remote-sensing imagery classification
Classification and identification of the materials lying over or beneath the earth's surface
have long been a fundamental but challenging research topic in geoscience and remote …
have long been a fundamental but challenging research topic in geoscience and remote …
[HTML][HTML] A survey: Deep learning for hyperspectral image classification with few labeled samples
With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …
Hyperspectral image classification with multi-attention transformer and adaptive superpixel segmentation-based active learning
Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
are widely used in hyperspectral image classification (HSIC). Some of these methods have …
Hyperspectral image classification—Traditional to deep models: A survey for future prospects
Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …
because it benefits from the detailed spectral information contained in each pixel. Notably …
Mutual information-driven pan-sharpening
M Zhou, K Yan, J Huang, Z Yang… - Proceedings of the …, 2022 - openaccess.thecvf.com
Pan-sharpening aims to integrate the complementary information of texture-rich PAN images
and multi-spectral (MS) images to produce the texture-rich MS images. Despite the …
and multi-spectral (MS) images to produce the texture-rich MS images. Despite the …
MHF-Net: An interpretable deep network for multispectral and hyperspectral image fusion
Multispectral and hyperspectral image fusion (MS/HS fusion) aims to fuse a high-resolution
multispectral (HrMS) and a low-resolution hyperspectral (LrHS) images to generate a high …
multispectral (HrMS) and a low-resolution hyperspectral (LrHS) images to generate a high …
Cross-scale mixing attention for multisource remote sensing data fusion and classification
Hyperspectral and multispectral images (HS/MS) fusion and classification as an important
branch of data quality improvement and interpretation have attracted increasing attention in …
branch of data quality improvement and interpretation have attracted increasing attention in …
A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification
Deep learning techniques have been widely applied to hyperspectral image (HSI)
classification and have achieved great success. However, the deep neural network model …
classification and have achieved great success. However, the deep neural network model …