Results 71 to 80 of about 2,902 (152)

Structure Extraction With Total Variation for Hyperspectral Image Classification

IEEE Access, 2019
This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV).
Qiaoqiao Li, Haibo Wang, Guoyue Chen, Kazuki Saruta, Yuki Terata   +4 more
doaj   +1 more source

Improving Hyperspectral Image Classification with Compact Multi-Branch Deep Learning

Remote Sensing
The progress in hyperspectral image (HSI) classification owes much to the integration of various deep learning techniques. However, the inherent 3D cube structure of HSIs presents a unique challenge, necessitating an innovative approach for the efficient
Md. Rashedul Islam, Md. Touhid Islam, Md Palash Uddin, Anwaar Ulhaq   +3 more
doaj   +1 more source

A Joint Network of 3D-2D CNN Feature Hierarchy and Pyramidal Residual Model for Hyperspectral Image Classification

IEEE Access
Since convolutional neural networks (CNN) can extract deeper features from hyperspectral images, they show good classification performance in the hyperspectral image (HSI) classification task. However, the performance of many CNN models is constrained by
Hongwei Wei, Yufan Wang, Yu Sun, Jianfeng Zheng, Xiaodong Yu   +4 more
doaj   +1 more source

An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification

Image Analysis and Stereology
Hyperspectral image (HSI) classification is a very important topic in remote sensing. There are many published methods for HSI classification in the literature. Nevertheless, it is not clear which method is the most robust to noise in HSI data cubes. In
Guangyi Chen, Adam Krzyzak, Shen-en Qian
doaj   +1 more source

AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification

Remote Sensing
Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively ...
Xiaofei Yang, Yuxiong Luo, Zhen Zhang, Dong Tang, Zheng Zhou, Haojin Tang   +5 more
doaj   +1 more source

Superpixel Random Selection Random Walk Multi-Branch Depthwise Convolutional Neural Network for Hyperspectral Image Classification. [PDF]

Sensors (Basel)
Zhang K, Jiang X, Cai Z.
europepmc   +1 more source

Hyperspectral image classification network based on multiscale spatial-spectral fusion and semantic enhancement encoder. [PDF]

Sci Rep
Yu J, Tang B, Zha C, Jiang N, Liu R, Zeng T, Ye Z, Feng F, Zhuang C.   +8 more
europepmc   +1 more source

Hyperspectral Fingerprints of Abdominal and Pelvic Organs. [PDF]

J Imaging
van de Weerd LS, van de Berg NJ, Rijstenberg LL, van de Laar RLO, van Beekhuizen HJ.   +4 more
europepmc   +1 more source

HS-RankFormer for efficient and robust RGB-to-Hyperspectral image reconstruction across domains. [PDF]

Sci Rep
Vamsi B, Mahanty M, Jagan BOL, Rai HM, Agarwal S, Pak W.   +5 more
europepmc   +1 more source

Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models. [PDF]

Sci Rep
Fathi G, Mireei SA, Jafari M, Sadeghi M, Karimmojeni H, Nazeri M, Tylewicz U.   +6 more
europepmc   +1 more source