Results 211 to 220 of about 9,650 (249)

Cascade Network for Hyperspectral Image Classification

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
Convolutional neural network (CNN) is one of the most powerful tools to deal with computer vision tasks such as hyperspectral image (HSI) classification. While many studies using CNN focus on classification precision, few of them pay attention to the model size and running time.
Shuai Fang, Wen Zhang, Jing Zhang 0037, Yang Cao 0010, Weikai Shi   +4 more
openaire   +1 more source

Multidomain Subspace Classification for Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2016
Hyperspectral imaging offers new opportunities for pattern recognition tasks in the remote sensing community through its improved discrimination in the spectral domain. However, such advanced image processing also brings new challenges due to the high data dimensionality in both the spatial and spectral domains. To relieve this issue, in this paper, we
Liangpei Zhang 0001, Xiaojie Zhu, Lefei Zhang, Bo Du 0001   +3 more
openaire   +1 more source

Hyperspectral image classification with hypergraph modelling

Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, 2012
Hyperspectral image classification requires a classifier which can deal with high-dimensional hyperspectral data. How to explore the relationship among different pixels in the hyperspetral image is essential for hyperspetral image classification. In this paper, we propose to formulate the correlation among pixels by using a hypergraph structure.
Yue Wen, Yue Gao 0002, Shaohui Liu, Qimin Cheng, Rongrong Ji   +4 more
openaire   +1 more source

Composite Kernels for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2006
This letter presents a framework of composite kernel machines for enhanced classification of hyperspectral images. This novel method exploits the properties of Mercer's kernels to construct a family of composite kernels that easily combine spatial and spectral information.
Gustavo Camps-Valls, Luis Gómez-Chova, Jordi Muñoz-Marí, Joan Vila-Francés, Javier Calpe-Maravilla   +4 more
openaire   +1 more source

Improved algorithm for hyperspectral image classification

Journal of Electronic Imaging, 2018
Due to the high-dimensional data space generated by hyperspectral sensors together with the real-time requirements of several remote sensing applications, it is important to accelerate hyperspectral data analysis. For this purpose, we aim to improve the performance of an existing classification algorithm and reduce its execution time.
Sonia Bouzidi, Houssem Ben Braiek
openaire   +1 more source

Accelerating classification time in Hyperspectral Images

2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015
K-nearest neighbour (K-NN) is a supervised classification technique that is widely used in many fields of study to classify unknown queries based on some known information about the dataset. K-NN is known to be robust and simple to implement when dealing with data of small size. However its performance is slow when data is large and has high dimensions.
Kemal Gurkan Toker, Seniha Esen Yüksel
openaire   +1 more source

Relation Network for Hyperspectral Image Classification

2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2019
In this paper, we design a simple, robust and powerful neural network architecture for hyperspectral image (HSI) classification, where state-of-the-art results can be achieved with only a small number of training samples. The proposed framework is a relation network (RN), whose objective is to learn the similarity between pairs of samples (pixels) in ...
Bin Deng, Daming Shi 0001
openaire   +1 more source

SRF: SpectrumRecombineFormer for Hyperspectral Image Classification

ACM Transactions on Multimedia Computing, Communications, and Applications
Hyperspectral imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral Imaging, such as high-dimensional features and information redundancy, pose significant challenges to data processing ...
Weipeng Jing 0001, Peilun Kang, Donglin Di, Juntao Gu, Linhui Li, Mahmoud Emam, Linda Mohaisen, Xun Yang 0001, Chao Li 0066   +8 more
openaire   +1 more source

Albedo recovery for hyperspectral image classification

Journal of Electronic Imaging, 2017
Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different from each other because of their intrinsic physical materials.
Kun Zhan, Haibo Wang, Yuange Xie, Chutong Zhang, Yufang Min   +4 more
openaire   +1 more source

Regularized methods for hyperspectral image classification

SPIE Proceedings, 2004
In this paper, we analyze regularized non-linear methods in the context of hyperspectral image classification. For this purpose, we compare regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVM), and kernel Fisher discriminant (KFD) analysis both theoretically and experimentally.
G. Camps Valls, Bruzzone, Lorenzo
openaire   +2 more sources