Spatial-spectral feature extraction on hyperspectral imagery

Kaufman, Jason R.; Weinheimer, J.; Celenk, Mehmet

doi:10.1109/whispers.2014.8077533

Cited by 5 publications

(10 citation statements)

References 9 publications

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“…Likewise, the high-resolution CCD imager produces a single broadband image over visible wavelengths. Drawing on a subset of atmospherically compensated imagery using the empirical line method reported in [18], we analyze the number of false alarms that occur via robust ANCFA when using each of the algorithms discussed in Section II.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…Meanwhile, targets such as the circles were inadvertently rotated during moving. These movements are summarized in Table I. In [18], we created pansharpened versions of two images each from the two Bobcat 2013 scenes listed in Table II and depicted in Fig. 6 with the PCA and GS pansharpening techniques.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…Previous applications of this metric [18] were limited to comparing individual pairs of target signatures extracted from the spatial location nearest to each target's centroid in F 0 and F 1 . We extend the ANCFA here to consider a single image-extracted spatial-spectral target signature against all target pixels in another feature image.…”

Section: E Scoringmentioning

confidence: 99%

“…1 were given in [18]. First, in order to address the void in suitable groundtruthed hyperspectral data to support quantitative spatialspectral target detection assessments, an experiment was designed and executed with the airborne real-time cueing hyperspectral enhanced reconnaissance (ARCHER) [19] sensor.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection

Kaufman

Eismann

Celenk

2015

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

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In this work, we assess the detection and classification of specially constructed targets in coincident airborne hyperspectral imagery (HSI) and high spatial resolution panchromatic imagery (HRI) in spectral, spatial, and joint spatial-spectral feature spaces. The target discrimination powers of the data-level and feature-level fusion of HSI and HRI are also directly compared in the spatial-spectral context using airborne imagery collected explicitly for this research. We show that in the case of Bobcat 2013 imagery, feature-level fusion of the HSI spectrum with spatial features derived from the coincident HRI data consistently results in fewer false alarms on the scene background as well as fewer misclassifications among the tested targets. Furthermore, this approach also outperforms schemes in which data-level fusion of the HSI and HRI imagery is performed prior to extracting spatial-spectral features.Index Terms-Hyperspectral imagery (HSI), image fusion, material identification, pansharpening, spatial-spectral feature extraction, target identification.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

Section: E Scoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection

Kaufman

Eismann

Celenk

2015

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

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“…Sample results from selected techniques appear in Figure 10. Our companion paper [7] evaluates the performance of various spatial-spectral feature extraction algorithms on this imagery. In the example given in Figure 11 we first apply the S-DAISY [8] technique (using parameters: radius = 21, rings = 3, petals = 8) to the SURE pansharpened imagery and the 8cm HRI data for the 20130408_170709_archer_hsi_0013 scan.…”

Section: Initial Data Exploitationmentioning

confidence: 99%

Bobcat 2013: a hyperspectral data collection supporting the development and evaluation of spatial-spectral algorithms

et al. 2014

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The amount of hyperspectral imagery (HSI) data currently available is relatively small compared to other imaging modalities, and what is suitable for developing, testing, and evaluating spatial-spectral algorithms is virtually nonexistent. In this work, a significant amount of coincident airborne hyperspectral and high spatial resolution panchromatic imagery that supports the advancement of spatial-spectral feature extraction algorithms was collected to address this need. The imagery was collected in April 2013 for Ohio University by the Civil Air Patrol, with their Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor. The target materials, shapes, and movements throughout the collection area were chosen such that evaluation of change detection algorithms, atmospheric compensation techniques, image fusion methods, and material detection and identification algorithms is possible. This paper describes the collection plan, data acquisition, and initial analysis of the collected imagery.

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Graph Attention Convolutional Autoencoder-Based Unsupervised Nonlinear Unmixing for Hyperspectral Images

Jin,

Yang

2023

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

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Hyperspectral unmixing has received increasing attention as a technique for estimating endmember spectra and fractional abundances of land covers. Encoding high-dimensional hyperspectral data into a low-dimensional latent space to generate reasonable abundances, autoencoder (AE) has shown its great potential and attractive advantages in spectral unmixing. AEs decode abundances back to spectra, which can effectively reflect the general spectral mixing process. However, most existing AE-based unmixing methods often do not fully exploit the spatial information of hyperspectral images, hindering the improvement of unmixing accuracy. This paper proposes a graph attention convolutional autoencoder architecture for hyperspectral unmixing. By incorporating graph attention convolution into AE, the proposed method performs better in leveraging both long-range and short-range spatial information of hyperspectral images. Accurate abundances with global and local spatial consistency can be efficiently learned by the network. Moreover, the decoder is further improved based on the postpolynomial nonlinear mixing (PPNM) model to make the network have stronger physical interpretability to deal with the issue of nonlinear blind unmixing. Experimental results indicate that the proposed method has good unmixing performance. It can reduce the root mean squared error of estimated abundances for synthetic data by over 10% compared to other methods. In experiments with real hyperspectral data, the difference between its unmixing results' accuracy and the best is less than 5%.

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Spatial-spectral feature extraction on hyperspectral imagery

Cited by 5 publications

References 9 publications

Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection

Assessment of Spatial–Spectral Feature-Level Fusion for Hyperspectral Target Detection

Bobcat 2013: a hyperspectral data collection supporting the development and evaluation of spatial-spectral algorithms

Graph Attention Convolutional Autoencoder-Based Unsupervised Nonlinear Unmixing for Hyperspectral Images

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