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Chein-i Chang - One of the best experts on this subject based on the ideXlab platform.
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Orthogonal Subspace projection based go decomposition approach to finding low rank and sparsity matrices for hyperspectral anomaly detection
IEEE Transactions on Geoscience and Remote Sensing, 2021Co-Authors: Chein-i Chang, Hongju Cao, Shuhan Chen, Xiaodi Shang, Meiping SongAbstract:Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order $m$ and sparsity cardinality $k$ . This article presents an Orthogonal Subspace-projection (OSP) version of GoDec to be called OSP-GoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining $p = m+ j$ and $k$ , the well-known virtual dimensionality (VD) is used to estimate $p$ in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum Orthogonal complement algorithm (MOCA) to estimate $k$ . Consequently, LRaSMD can be realized by implementing OSP-GoDec using $p$ and $k$ determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.
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gpu implentation of recursive automatic target generation process and recursive Orthogonal Subspace projection in hyperspectral imagery
Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2018Co-Authors: Meiping Song, Jin Huang, Chein-i ChangAbstract:Despite of the fact that recursive automatic target generation process (RATGP) and recursive Orthogonal Subspace projection (ROSP) have played a crucial role in a hyperspectral unmixing field. However, it’s hard to process rapidly in terms of the amounts of data in a hyperspectral remote sensing image. With the feature of graphic processing units (GPUs), calculating a large-scale parallel computing, the paper was studied through the method of parallel and optimization to the RATGP and ROSP algorithm. Therefore, we proposed a parallel design method based on matrix operation. The experiment results showed that the mechanism of optimization has been greatly improved the efficiency of the algorithm in a real hyperspectral image under the condition of keeping precision of unmixing results.
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band specified virtual dimensionality for band selection an Orthogonal Subspace projection approach
IEEE Transactions on Geoscience and Remote Sensing, 2018Co-Authors: Lichien Lee, Chein-i Chang, Meiping Song, Bai Xue, Jian ChenAbstract:This paper develops a new Neyman–Pearson detection approach, to be called band-specified virtual dimensionality (BSVD), to estimating the number of bands required by band selection (BS), $n_{\mathrm {BS}}$ , as well as finding desired bands at the same time. Its idea is derived from target-specified virtual dimensionality (TSVD) where targets under hypotheses as signal sources in TSVD are replaced with bands as signal sources and the test statistics derived for a Neyman–Pearson detector (NPD) is signal-to-noise ratio (SNR) that is used to derive Orthogonal Subspace projection (OSP) approach for hyperspectral image classification and dimensionality reduction. Accordingly, the resulting virtual dimensionality is referred to as OSP-based BSVD. Several benefits resulting from BSVD cannot be offered by the traditional BS methods. One is its direct approach to dealing with $n_{\mathrm {BS}}$ . Another is no-search strategy needed for finding optimal bands. Instead, it uses NPD to determine and rank desired bands for band prioritization. Most importantly, it determines $n_{\mathrm {BS}}$ and finds desired bands simultaneously and progressively.
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WHISPERS - GPU Implentation of Recursive Automatic Target Generation Process and Recursive Orthogonal Subspace Projection in Hyperspectral Imagery
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018Co-Authors: Meiping Song, Jin Huang, Chein-i ChangAbstract:Despite of the fact that recursive automatic target generation process (RATGP) and recursive Orthogonal Subspace projection (ROSP) have played a crucial role in a hyperspectral unmixing field. However, it’s hard to process rapidly in terms of the amounts of data in a hyperspectral remote sensing image. With the feature of graphic processing units (GPUs), calculating a large-scale parallel computing, the paper was studied through the method of parallel and optimization to the RATGP and ROSP algorithm. Therefore, we proposed a parallel design method based on matrix operation. The experiment results showed that the mechanism of optimization has been greatly improved the efficiency of the algorithm in a real hyperspectral image under the condition of keeping precision of unmixing results.
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Recursive Hyperspectral Sample Processing of Orthogonal Subspace Projection
Real-Time Recursive Hyperspectral Sample and Band Processing, 2017Co-Authors: Chein-i ChangAbstract:Orthogonal Subspace projection (OSP) developed by Harsanyi and Chang (IEEE Transactions on Geoscience and Remote Sensing 32:779–785, 1994) (see Hyperspectral image: spectral techniques for detection and classification, Kluwer Academic Publishers, New York, 2003; Hyperspectral data processing: algorithm design and analysis, Wiley, Hoboken, 2013) has found its potential in many hyperspectral data exploitation applications. It works in two stages: an OSP-based projector to annihilate undesired signal sources in the first stage, to improve background suppression so as to increase target detectability, followed by a matched filter in the second stage, to extract the desired signal source for target enhancement. However, for OSP to be effective it assumes that the signal sources are provided a priori. As a result, OSP can only be used as a supervised algorithm. In many real-world applications, there are many unknown signal sources that can be revealed by hyperspectral imaging sensors. It is highly desirable to extend OSP to an unsupervised version, called unsupervised OSP (UOSP) developed by Wang et al. (Optical Engineering 41:1546–1557, 2002), where the signal sources used for OSP can be found in an unsupervised manner. An issue arising in UOSP is how to determine the number of such found unsupervised signal sources, which must be known in advance. This chapter further extends UOSP to progressive OSP (P-OSP) so that P-OSP can not only generate a growing set of new unknown signal sources one at a time progressively but can also determine the number of unknown signal sources to be generated while OSP processing is taking place. Since the unknown signal sources generated by P-OSP remain unchanged after they are generated, OSP should be able to take advantage of it without reprocessing these signal sources. This leads to a new development of a recursive version of OSP, called recursive hyperspectral sample processing of OSP (RHSP-OSP).
Meiping Song - One of the best experts on this subject based on the ideXlab platform.
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Orthogonal Subspace projection based go decomposition approach to finding low rank and sparsity matrices for hyperspectral anomaly detection
IEEE Transactions on Geoscience and Remote Sensing, 2021Co-Authors: Chein-i Chang, Hongju Cao, Shuhan Chen, Xiaodi Shang, Meiping SongAbstract:Low-rank and sparsity-matrix decomposition (LRaSMD) has received considerable interests lately. One of effective methods for LRaSMD is called go decomposition (GoDec), which finds low-rank and sparse matrices iteratively subject to the predetermined low-rank matrix order $m$ and sparsity cardinality $k$ . This article presents an Orthogonal Subspace-projection (OSP) version of GoDec to be called OSP-GoDec, which implements GoDec in an iterative process by a sequence of OSPs to find desired low-rank and sparse matrices. In order to resolve the issues of empirically determining $p = m+ j$ and $k$ , the well-known virtual dimensionality (VD) is used to estimate $p$ in conjunction with the Kuybeda et al. developed minimax-singular value decomposition (MX-SVD) in the maximum Orthogonal complement algorithm (MOCA) to estimate $k$ . Consequently, LRaSMD can be realized by implementing OSP-GoDec using $p$ and $k$ determined by VD and MX-SVD, respectively. Its application to anomaly detection demonstrates that the proposed OSP-GoDec coupled with VD and MX-SVD performs very effectively and better than the commonly used LRaSMD-based anomaly detectors.
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gpu implentation of recursive automatic target generation process and recursive Orthogonal Subspace projection in hyperspectral imagery
Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2018Co-Authors: Meiping Song, Jin Huang, Chein-i ChangAbstract:Despite of the fact that recursive automatic target generation process (RATGP) and recursive Orthogonal Subspace projection (ROSP) have played a crucial role in a hyperspectral unmixing field. However, it’s hard to process rapidly in terms of the amounts of data in a hyperspectral remote sensing image. With the feature of graphic processing units (GPUs), calculating a large-scale parallel computing, the paper was studied through the method of parallel and optimization to the RATGP and ROSP algorithm. Therefore, we proposed a parallel design method based on matrix operation. The experiment results showed that the mechanism of optimization has been greatly improved the efficiency of the algorithm in a real hyperspectral image under the condition of keeping precision of unmixing results.
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band specified virtual dimensionality for band selection an Orthogonal Subspace projection approach
IEEE Transactions on Geoscience and Remote Sensing, 2018Co-Authors: Lichien Lee, Chein-i Chang, Meiping Song, Bai Xue, Jian ChenAbstract:This paper develops a new Neyman–Pearson detection approach, to be called band-specified virtual dimensionality (BSVD), to estimating the number of bands required by band selection (BS), $n_{\mathrm {BS}}$ , as well as finding desired bands at the same time. Its idea is derived from target-specified virtual dimensionality (TSVD) where targets under hypotheses as signal sources in TSVD are replaced with bands as signal sources and the test statistics derived for a Neyman–Pearson detector (NPD) is signal-to-noise ratio (SNR) that is used to derive Orthogonal Subspace projection (OSP) approach for hyperspectral image classification and dimensionality reduction. Accordingly, the resulting virtual dimensionality is referred to as OSP-based BSVD. Several benefits resulting from BSVD cannot be offered by the traditional BS methods. One is its direct approach to dealing with $n_{\mathrm {BS}}$ . Another is no-search strategy needed for finding optimal bands. Instead, it uses NPD to determine and rank desired bands for band prioritization. Most importantly, it determines $n_{\mathrm {BS}}$ and finds desired bands simultaneously and progressively.
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WHISPERS - GPU Implentation of Recursive Automatic Target Generation Process and Recursive Orthogonal Subspace Projection in Hyperspectral Imagery
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018Co-Authors: Meiping Song, Jin Huang, Chein-i ChangAbstract:Despite of the fact that recursive automatic target generation process (RATGP) and recursive Orthogonal Subspace projection (ROSP) have played a crucial role in a hyperspectral unmixing field. However, it’s hard to process rapidly in terms of the amounts of data in a hyperspectral remote sensing image. With the feature of graphic processing units (GPUs), calculating a large-scale parallel computing, the paper was studied through the method of parallel and optimization to the RATGP and ROSP algorithm. Therefore, we proposed a parallel design method based on matrix operation. The experiment results showed that the mechanism of optimization has been greatly improved the efficiency of the algorithm in a real hyperspectral image under the condition of keeping precision of unmixing results.
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Recursive Band Processing of Orthogonal Subspace Projection for Hyperspectral Imagery
IEEE Geoscience and Remote Sensing Letters, 2016Co-Authors: Chein-i Chang, Meiping SongAbstract:Recursive band processing of Orthogonal Subspace projection (RBP-OSP) is developed according to the band sequential (BSQ) format acquired by a hyperspectral imaging sensor. It can be implemented band by band recursively without waiting for data being completely collected. This is particularly important for satellite communication when data download is limited by bandwidth and transmission. Unlike band selection which requires prior knowledge of how many bands are needed to be selected, RBP-OSP has capability which allows different process units to process data whenever bands are available. In addition, it also enables users to identify significant bands during data processing. Finally and most importantly, RBP can provide progressive profiles on OSP performance, which is the best advantage that RBP-OSP can offer and cannot be accomplished by any one-shot operator.
Hsuan Ren - One of the best experts on this subject based on the ideXlab platform.
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A comparative study and analysis between vertex component analysis and Orthogonal Subspace projection for endmember extraction
Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XIII, 2007Co-Authors: Wei-min Liu, Hsuan Ren, Chein-i ChangAbstract:Endmember extraction has received considerable interest in recent years. Of particular interest is the Pixel Purity Index (PPI) because of its publicity and availability in ENVI software. There are also many variants of the PPI have been developed. Among them is an interesting endmember extraction algorithm (EEA), called vertex component analysis (VCA) developed by Dias and Nascimento who extend the PPI to a simplex-based EEA while using Orthogonal Subspace projection (OSP) as a projection criterion rather than simplex volume used by another well-known EEA, N-finder algorithm (N-FINDR) developed by Winter. Interestingly, this paper will show that the VCA is essentially the same algorithm, referred to as Automatic Target Generation Process (ATGP) recently developed for automatic target detection and classification by Ren and Chang except the use of the initial condition to initialize the algorithm. In order to substantiate our findings, experiments using synthetic and real images are conducted for a comparative study and analysis.
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Comparison between Orthogonal Subspace projection and background subtraction techniques applied to remote-sensing data
Applied optics, 2005Co-Authors: Avishai Ben-david, Hsuan RenAbstract:The basic measurement equation r = B + αd + n is solved for α (the weight or abundance of the spectral target vector d) by two methods: (a) by subtracting the stochastic spectral background vector B from the spectral measurement’s vector r (subtraction solution) and (b) by Orthogonal Subspace projection (OSP) of the measurements to a Subspace Orthogonal to B (the OSP solution). The different geometry of the two solutions and in particular the geometry of the noise vector n is explored. The angular distribution of the noise angle between B and n is the key factor for determining and predicting which solution is better. When the noise-angle distribution is uniform, the subtraction solution is always superior regardless of the orientation of the spectral target vector d. When the noise is more concentrated in the direction parallel to B, the OSP solution becomes better (as expected). Simulations and one-dimensional hyperspectral measurements of vapor concentration in the presence of background radiation and noise are given to illustrate these two solutions.
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A comparative study for Orthogonal Subspace projection and constrained energy minimization
IEEE Transactions on Geoscience and Remote Sensing, 2003Co-Authors: Hsuan Ren, Chein-i ChangAbstract:We conduct a comparative study and investigate the relationship between two well-known techniques in hyperspectral image detection and classification: Orthogonal Subspace projection (OSP) and constrained energy minimization. It is shown that they are closely related and essentially equivalent provided that the noise is white with large SNR. Based on this relationship, the performance of OSP can be improved via data-whitening and noise-whitening processes.
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a generalized Orthogonal Subspace projection approach to unsupervised multispectral image classification
IEEE Transactions on Geoscience and Remote Sensing, 2000Co-Authors: Hsuan Ren, Chein-i ChangAbstract:Orthogonal Subspace projection (OSP) has been successfully applied in hyperspectral image processing. In order for the OSP to be effective, the number of bands must be no less than that of signatures to be classified. This ensures that there are sufficient dimensions to accommodate Orthogonal projections resulting from the individual signatures. Such inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands such as three-band pour l'observation de la terra (SPOT) images. This paper presents a generalization of the OSP called generalized OSP (GOSP) that relaxes this constraint in such a manner that the OSP can be extended to multispectral image processing in an unsupervised fashion. The idea of the GOSP is to create a new set of additional bands that are generated nonlinearly from original multispectral bands prior to the OSP classification. It is then followed by an unsupervised OSP classifier called automatic target detection and classification algorithm (ATDCA). The effectiveness of the proposed GOSP is evaluated by SPOT and Landsat TM images. The experimental results show that the GOSP significantly improves the classification performance of the OSP.
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Generalized Orthogonal Subspace projection approach to multispectral image classification
Image and Signal Processing for Remote Sensing IV, 1998Co-Authors: Hsuan Ren, Chein-i ChangAbstract:Orthogonal Subspace projection (OSP) has been successfully applied to hyperspectral image processing. In order for OSP to be effective, the number of bands must be no less than that of signatures to be classified so that there are sufficient dimensions to accommodate individual signatures to discriminate one another via Orthogonal projection. This intrinsic constraint is not an issue for hyperspectral images since they generally have hundreds of bands which are more than the number of signatures resident within images. It, however, may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands such as 3-band SPOT images. This paper presents a generalization of OSP, called generalized OSP (GOSP) to relax this constraint in such a fashion that OSP can be extended to multispectral image processing in an unsupervised fashion. The idea of GOSP is to create new additional band images nonlinearly from original multispectral images so as to achieve sufficient dimensionality prior to OSP classification. It is then followed by an unsupervised OSP classifier, called automatic target detection and classification algorithm (ATDCA) for classification. The effectiveness of the proposed GOSP is evaluated by a 3-band SPOT and a 4-band Landsat MSS images. The experimental results has shown that GOSP significantly improves the classification performance of OSP.
Steven Johnson - One of the best experts on this subject based on the ideXlab platform.
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Comments on "Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis
IEEE Transactions on Geoscience and Remote Sensing, 2007Co-Authors: Steven JohnsonAbstract:A recent paper discussed the Orthogonal Subspace projection (OSP) algorithm. That paper advocated the use of OSP for target detection and material abundance estimation for mixed pixels in hyperspectral images. However, it was proved in an older paper that an algorithm called the constrained signal detector (CSD) generally outperforms the OSP for the applications of target detection and abundance estimation. The purpose of this correspondence is to review the differences in detection and estimation performance between OSP and CSD
Nasser M. Nasrabadi - One of the best experts on this subject based on the ideXlab platform.
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Kernel Orthogonal Subspace projection for hyperspectral signal classification
IEEE Transactions on Geoscience and Remote Sensing, 2005Co-Authors: Heesung Kwon, Nasser M. NasrabadiAbstract:In this paper, a kernel-based nonlinear version of the Orthogonal Subspace projection (OSP) operator is defined in terms of kernel functions. Input data are implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The OSP expression is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. The resulting kernelized OSP algorithm is equivalent to a nonlinear OSP in the original input space. Experimental results are presented for detection of roads, roof tops, mines, and targets in hyperspectral imagery, and it is shown that the kernelized OSP method outperforms the conventional OSP approach.
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ICIP (2) - Hyperspectral target detection using kernel Orthogonal Subspace projection
IEEE International Conference on Image Processing 2005, 2005Co-Authors: Heesung Kwon, Nasser M. NasrabadiAbstract:In this paper, a kernel-based nonlinear version of the Orthogonal Subspace projection (OSP) classifier is defined in terms of kernel functions. Input data is implicitly mapped into a high dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The OSP expression is then derived in the feature space which is kernelized in terms of kernel functions in order to avoid explicit computation in the high dimensional feature space. The resulting kernelized OSP algorithm is equivalent to a nonlinear OSP in the original input space. Experimental results are presented for target detection in hyperspectral imagery and it is shown that the kernel OSP outperforms the conventional OSP classifier.
