Results 31 to 40 of about 15,264 (300)
Advances in Electrical and Computer Engineering, 2017 Hyperspectral pixels which have high spectral resolution are used to predict decomposition of material types on area of obtained image. Due to its multidimensional form, hyperspectral image classification is a challenging task. Hyperspectral images are
AKYUREK, H. A., KOCER, B.
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SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Owing to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image ...
Hong, Danfeng +6 more
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Remote Sensing, 2019 In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method
Jianshang Liao, Liguo Wang
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Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data [PDF]
, 2009 Hyperspectral data allows the construction of more elaborate models to sample the properties of the nonferrous materials than the standard RGB color representation.
Iriondo, Pedro M. +4 more
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Hyperspectral Image Classification With Attention-Aided CNNs [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2021 Convolutional neural networks (CNNs) have been widely used for hyperspectral image classification. As a common process, small cubes are firstly cropped from the hyperspectral image and then fed into CNNs to extract spectral and spatial features.
Renlong Hang +4 more
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Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs
Remote Sensing, 2018 Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify
Feng Gao, Qun Wang, Junyu Dong, Qizhi Xu
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Quality criteria benchmark for hyperspectral imagery [PDF]
, 2005 Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community.
Christophe, Emmanuel +2 more
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Mahalanobis kernel for the classification of hyperspectral images [PDF]
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010 The definition of the Mahalanobis kernel for the classification of hyperspectral remote sensing images is addressed. Class specific covariance matrices are regularized by a probabilistic model which is based on the data living in a subspace spanned by the p first principal components.
Fauvel, Mathieu +3 more
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A DIVERSIFIED DEEP BELIEF NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 In recent years, researches in remote sensing demonstrated that deep architectures with multiple layers can potentially extract abstract and invariant features for better hyperspectral image classification. Since the usual real-world hyperspectral image
P. Zhong, Z. Q. Gong, C. Schönlieb
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Effective classification of Chinese tea samples in hyperspectral imaging
, 2013 Maximum likelihood and neural classifiers are two typical techniques in image classification. This paper investigates how to adapt these approaches to hyperspectral imaging for the classification of five kinds of Chinese tea samples, using visible light ...
Ren, Jinchang +2 more
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