Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tunning and challenges
Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …
Deep learning methods for solving linear inverse problems: Research directions and paradigms
The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …
Innumerable attempts have been carried out to solve different variants of the linear inverse …
Power quality disturbances detection and classification based on deep convolution auto-encoder networks
Power quality issues are required to be addressed properly in forthcoming era of smart
meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto …
meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto …
A survey on superpixel segmentation as a preprocessing step in hyperspectral image analysis
Recent developments in hyperspectral sensors have made it possible to acquire
hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now …
hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now …
A pipeline defect inversion method with erratic MFL signals based on cascading abstract features
H Zhang, L Wang, J Wang, F Zuo… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Defect inversion, as a key step in magnetic flux leakage (MFL) inspection widely used in
nondestructive testing (NDT) systems, is critical to quantitative analysis of pipeline risk level …
nondestructive testing (NDT) systems, is critical to quantitative analysis of pipeline risk level …
Fused 3-D spectral-spatial deep neural networks and spectral clustering for hyperspectral image classification
Recently, classification and dimensionality reduction (DR) have become important issues of
hyperspectral image (HSI) analysis. Especially, HSI classification is a challenging task due …
hyperspectral image (HSI) analysis. Especially, HSI classification is a challenging task due …
Hyperspectral meets optical flow: Spectral flow extraction for hyperspectral image classification
B Liu, Y Sun, A Yu, Z Xue, X Zuo - IEEE Transactions on Image …, 2023 - ieeexplore.ieee.org
Hyperspectral image (HSI) classification has always been recognised as a difficult task. It is
therefore a research hotspot in remote sensing image processing and analysis, and a …
therefore a research hotspot in remote sensing image processing and analysis, and a …
ES2FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images
B Liu, K Gao, A Yu, L Ding, C Qiu, J Li - Remote Sensing, 2022 - mdpi.com
Classification with a few labeled samples has always been a longstanding problem in the
field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample …
field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample …
Adjacent superpixel-based multiscale spatial-spectral kernel for hyperspectral classification
The kernel-based spatial-spectral approach has been widely used for hyperspectral image
(HSI) classification in recent years, where composite kernel (CK) and spatial-spectral kernel …
(HSI) classification in recent years, where composite kernel (CK) and spatial-spectral kernel …
Deep residual involution network for hyperspectral image classification
Z Meng, F Zhao, M Liang, W Xie - Remote Sensing, 2021 - mdpi.com
Convolutional neural networks (CNNs) have achieved great results in hyperspectral image
(HSI) classification in recent years. However, convolution kernels are reused among …
(HSI) classification in recent years. However, convolution kernels are reused among …