Results 21 to 30 of about 9,650 (249)
Supervised hyperspectral image classification with rejection
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015 Hyperspectral image classification is a challenging problem as obtaining complete and representative training sets is costly, pixels can belong to unknown classes, and it is generally an ill-posed problem. The need to achieve high classification accuracy may surpass the need to classify the entire image.
Filipe Condessa +2 more
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A fuzzy spectral clustering algorithm for hyperspectral image classification
IET Image Processing, 2021 Spectral clustering is an unsupervised clustering algorithm, and is widely used in the field of pattern recognition and computer vision due to its good clustering performance.
Kang Li +3 more
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Remote Sensing, 2022 Hyperspectral image (HSI) classification is one of the main research contents of hyperspectral technology. Existing HSI classification algorithms that are based on deep learning use a large number of labeled samples to train models to ensure excellent ...
Shuhan Zhang +5 more
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JigsawHSI: a network for Hyperspectral Image classification
CoRR, 2022 7 pages, 7 figures, not peer ...
Jaime Moraga, H. Sebnem Düzgün
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Classification Endmember Selection with Multi-Temporal Hyperspectral Data
Remote Sensing, 2020 In hyperspectral image classification, so-called spectral endmembers are used as reference data. These endmembers are either extracted from an image or taken from another source.
Tingxuan Jiang +2 more
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A CNN with noise inclined module and denoise framework for hyperspectral image classification
IET Image Processing, 2023 Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical noise ...
Zhiqiang Gong +5 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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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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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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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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