CBANet: An end-to-end cross-band 2-D attention network for hyperspectral change detection in remote sensing
As a fundamental task in remote sensing (RS) observation of the earth, change detection
(CD) using hyperspectral images (HSI) features high accuracy due to the combination of the …
(CD) using hyperspectral images (HSI) features high accuracy due to the combination of the …
Novel adaptive region spectral–spatial features for land cover classification with high spatial resolution remotely sensed imagery
Spectral–spatial features are important for ground target identification and classification with
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …
[HTML][HTML] Deep learning techniques for hyperspectral image analysis in agriculture: A review
In recent years, there has been a growing emphasis on assessing and ensuring the quality
of horticultural and agricultural produce. Traditional methods involving field measurements …
of horticultural and agricultural produce. Traditional methods involving field measurements …
Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images
Despite various approaches proposed to smooth the hyperspectral images (HSIs) before
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
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) …
An unsupervised domain adaptation method towards multi-level features and decision boundaries for cross-scene hyperspectral image classification
C Zhao, B Qin, S Feng, W Zhu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-
scene classification, samples between source and target scenes are not drawn from the …
scene classification, samples between source and target scenes are not drawn from the …
A novel band selection and spatial noise reduction method for hyperspectral image classification
As an essential reprocessing method, dimensionality reduction (DR) can reduce the data
redundancy and improve the performance of hyperspectral image (HSI) classification. A …
redundancy and improve the performance of hyperspectral image (HSI) classification. A …
Dual-branch subpixel-guided network for hyperspectral image classification
Deep learning (DL) has been widely applied to hyperspectral image (HSI) classification,
owing to its promising feature learning and representation capabilities. However, limited by …
owing to its promising feature learning and representation capabilities. However, limited by …
Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed MobileNet
R Chen, H Huang, Y Yu, J Ren, P Wang… - IEEE internet of …, 2023 - ieeexplore.ieee.org
Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code
decoding-based Internet of Things (IoT) systems. To tackle this issue, we propose in this …
decoding-based Internet of Things (IoT) systems. To tackle this issue, we propose in this …
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 …