Results 201 to 210 of about 49,813 (252)

Fusion of circulant singular spectrum analysis and multiscale local ternary patterns for effective spectral-spatial feature extraction and small sample hyperspectral image classification. [PDF]

Sci Rep
Wan X, Chen F, Gao W, Mo D, Liu H.
europepmc   +1 more source

A Dual-Branch Fusion of a Graph Convolutional Network and a Convolutional Neural Network for Hyperspectral Image Classification. [PDF]

Sensors (Basel)
Yang P, Zhang X.
europepmc   +1 more source

Attention 3D central difference convolutional dense network for hyperspectral image classification. [PDF]

PLoS One
Ashraf M, Alharthi R, Chen L, Umer M, Alsubai S, Eshmawi AA.   +5 more
europepmc   +1 more source

Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification. [PDF]

Sci Rep
Sigger N, Vien QT, Nguyen SV, Tozzi G, Nguyen TT.   +4 more
europepmc   +1 more source

Transfer Learning-Based Hyperspectral Image Classification Using Residual Dense Connection Networks. [PDF]

Sensors (Basel)
Zhou H, Wang X, Xia K, Ma Y, Yuan G.
europepmc   +1 more source

CESA-MCFormer: An Efficient Transformer Network for Hyperspectral Image Classification by Eliminating Redundant Information. [PDF]

Sensors (Basel)
Liu S, Yin C, Zhang H.
europepmc   +1 more source

DMAF-NET: Deep Multi-Scale Attention Fusion Network for Hyperspectral Image Classification with Limited Samples. [PDF]

Sensors (Basel)
Guo H, Liu W.
europepmc   +1 more source

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Kernel-based methods for hyperspectral image classification

IEEE Transactions on Geoscience and Remote Sensing, 2005
This paper presents the framework of kernel-based methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernel-based approaches and analyzing their properties in the hyperspectral domain.
G. Camps valls, Bruzzone, Lorenzo
openaire   +4 more sources

Superpixel based classification of hyperspectral images

2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015
Hyperspectral imaging captures a high number of spectrally narrow bands and provides advantages for image analysis applications such as identification and classification in particular. Hyperspectral images contain a large amount of bands. Processing these images causes the operation load substantially.
ERTÜRK, SARP, Cezairlioglu, Kubra, Cakmak, Mehtap   +2 more
openaire   +2 more sources

Optimizing wavelets for hyperspectral image classification

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
This work presents a procedure to optimize a wavelet filter in terms of discrimination capability between the classes characterizing a given hyperspectral remote sensing image. To this end, this procedure estimates the coefficients of the wavelet filter bank by means of a particle swarm optimization (PSO) so that to maximize the average Bhattacharyya ...
A. Daamouche, Melgani, Farid, L. Hamami
openaire   +2 more sources