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Hierarchical classification systems for hyperspectral image classification
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007In this study, we proposed some alternatives for building a binary hierarchical classification (BHC) systems. Two criteria for building the hierarchical tree under the idea of max-cut are addressed and two additional classification architectures based on the constructed trees are also proposed.
Bor-Chen Kuo +3 more
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Dimensionality reduction in hyperspectral image classification
2004 International Conference on Image Processing, 2004. ICIP '04., 2005Hyperspectral images provide a vast amount of information about a scene. However, much of that information is redundant as the bands are highly correlated. For computational and data compression reasons, it is desired to reduce the dimensionality of the data set while maintaining good performance in image analysis tasks.
Huiwen Zeng, H. Joel Trussell
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Cascade Network for Hyperspectral Image Classification
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021Convolutional 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 +4 more
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Multidomain Subspace Classification for Hyperspectral Images
IEEE Transactions on Geoscience and Remote Sensing, 2016Hyperspectral 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 +3 more
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Hyperspectral image classification with hypergraph modelling
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, 2012Hyperspectral 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 +4 more
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Composite Kernels for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2006This 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 +4 more
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Improved algorithm for hyperspectral image classification
Journal of Electronic Imaging, 2018Due 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
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Accelerating classification time in Hyperspectral Images
2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015K-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
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Relation Network for Hyperspectral Image Classification
2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2019In 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
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Albedo recovery for hyperspectral image classification
Journal of Electronic Imaging, 2017Image 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 +4 more
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