Attention mechanism and depthwise separable convolution aided 3DCNN for hyperspectral remote sensing image classification
Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural
Network (CNN) has become one of the hot topics in the field of remote sensing. However …
Network (CNN) has become one of the hot topics in the field of remote sensing. However …
Towards on-board hyperspectral satellite image segmentation: Understanding robustness of deep learning through simulating acquisition conditions
Although hyperspectral images capture very detailed information about the scanned objects,
their efficient analysis, transfer, and storage are still important practical challenges due to …
their efficient analysis, transfer, and storage are still important practical challenges due to …
Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning
Some plant diseases can significantly reduce harvest, but their early detection in cultivation
may prevent those consequential losses. Conventional methods of diagnosing plant …
may prevent those consequential losses. Conventional methods of diagnosing plant …
Triple-attention-based parallel network for hyperspectral image classification
Convolutional neural networks have been highly successful in hyperspectral image
classification owing to their unique feature expression ability. However, the traditional data …
classification owing to their unique feature expression ability. However, the traditional data …
Unbiasing the estimation of chlorophyll from hyperspectral images: a benchmark dataset, validation procedure and baseline results
Recent advancements in hyperspectral remote sensing bring exciting opportunities for
various domains. Precision agriculture is one of the most widely-researched examples here …
various domains. Precision agriculture is one of the most widely-researched examples here …
Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis
Z Zheng, S Zhang, H Song, Q Yan - Scientific Reports, 2024 - nature.com
Deep clustering has been widely applicated in various fields, including natural image and
language processing. However, when it is applied to hyperspectral image (HSI) processing …
language processing. However, when it is applied to hyperspectral image (HSI) processing …
Hyperspectral band selection via band grouping and adaptive multi-graph constraint
Unsupervised band selection has gained increasing attention recently since massive
unlabeled high-dimensional data often need to be processed in the domains of machine …
unlabeled high-dimensional data often need to be processed in the domains of machine …
A band subset selection approach based on sparse self-representation and band grouping for hyperspectral image classification
KH Liu, YK Chen, TY Chen - Remote Sensing, 2022 - mdpi.com
Band subset selection (BSS) is one of the ways to implement band selection (BS) for a
hyperspectral image (HSI). Different from conventional BS methods, which select bands one …
hyperspectral image (HSI). Different from conventional BS methods, which select bands one …
SC-CAN: spectral convolution and Channel Attention network for wheat stress classification
Biotic and abiotic plant stress (eg, frost, fungi, diseases) can significantly impact crop
production. It is thus essential to detect such stress at an early stage before visual symptoms …
production. It is thus essential to detect such stress at an early stage before visual symptoms …
A multiscale dilated attention network for hyperspectral image classification
C Tu, W Liu, W Jiang, L Zhao, T Yan - Advances in Space Research, 2024 - Elsevier
Hyperspectral imaging is an image obtained by combining spectral detection technology
and imaging technology, which can collect electromagnetic spectra in the wavelength range …
and imaging technology, which can collect electromagnetic spectra in the wavelength range …