Supervised Functional Data Discriminant Analysis for Hyperspectral Image Classification

Ye, Zhijing; Chen, Jiaqing; Li, Hong; Wei, Yantao; Xiao, Guangrun; Benediktsson, Jón Atli

doi:10.1109/tgrs.2019.2940991

Cited by 12 publications

(6 citation statements)

References 41 publications

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“…• the r can be investigated through the combination of [1,2,3], [2,4,6], [4,6,8], and [6, 8, 10] 1 ; • the order m can be selected from the different set, i.e., [0, 1], [0, 1, 2], [0, 1, 2, 3], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4, 5], . .…”

Section: Parameter Sensitivity Analysismentioning

confidence: 99%

“…L AND use and land cover (LULC) classification has been playing an increasingly vital role in the high-level image interpretation and analysis of remote sensing [1], [2]. Owing to the rich spectral information, hyperspectral imagery (HSI) enables the identification and detection of the materials at a more accurate level, which has been proven to be effective for LULC-related tasks, such as HSI classification [3], [4], [5], [6], multi-modality data analysis [7], [8], [9], [10], and anomaly detection [11], [12], [13]. In spite of the fine spectral discrepancies in HSI, the noisy pixels, manual labeling uncertainty, and the intrinsic or extrinsic spectral variability inevitably degrade the classification performance when only the spectral profile is considered as the feature input.…”

Section: Introductionmentioning

confidence: 99%

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Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Hong,

Wu,

Ghamisi

et al. 2019

Preprint

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This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Up to the present, an enormous number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. As a consequence, identifying the same materials from spatially different scenes or positions can be difficult. In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of spatial-frequency invariant features with application to HSI classification. Extensive experiments conducted on three promising hyperspectral datasets (Houston2013 and Houston2018) demonstrate the superiority and effectiveness of the proposed IAP method in comparison with several state-of-the-art profile-related techniques. The codes will be

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Section: Parameter Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Hong,

Wu,

Ghamisi

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…L AND use and land cover (LULC) classification has been playing an increasingly vital role in the high-level image interpretation and analysis of remote sensing [1], [2]. Owing to the rich spectral information, hyperspectral imagery (HSI) enables the identification and detection of the materials at a more accurate level, which has been proven to be effective for LULC-related tasks, such as HSI classification [3]- [6], multimodality data analysis [7]- [10], and anomaly detection [11]- [13]. In spite of the fine spectral discrepancies in HSI, the noisy pixels, manual labeling uncertainty, and the intrinsic or extrinsic spectral variability inevitably degrade the classification performance when only the spectral profile is considered as the feature input.…”

Section: Introductionmentioning

confidence: 99%

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Hong

Ghamisi

et al. 2020

IEEE Trans. Geosci. Remote Sensing

264

View full text Add to dashboard Cite

So far, a large number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. Consequently, identifying the same materials from spatially different scenes or positions can be difficult. In this article, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of

show abstract

“…Over the past decades, various feature extraction methods have been proposed for HSI classification [7]- [9]. In the early stage of the study on HSI feature extraction, many methods extract features from the spectral domain, which show poor classification accuracy [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Learning a Deep Similarity Network for Hyperspectral Image Classification

Yang

Guo

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Supervised Functional Data Discriminant Analysis for Hyperspectral Image Classification

Cited by 12 publications

References 41 publications

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

Learning a Deep Similarity Network for Hyperspectral Image Classification

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