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26
Hyperspectral Remote Sensing Data Analysis and Future Challenges
"... Abstract—Hyperspectral remote sen-sing ..."
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Outlier Detection with the Kernelized Spatial Depth Function
, 2008
"... Statistical depth functions provide from the “deepest ” point a “center-outward ordering” of multidimensional data. In this sense, depth functions can measure the “extremeness” or “outlyingness” of a data point with respect to a given data set. Hence they can detect outliers – observations that appe ..."
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Cited by 22 (4 self)
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Statistical depth functions provide from the “deepest ” point a “center-outward ordering” of multidimensional data. In this sense, depth functions can measure the “extremeness” or “outlyingness” of a data point with respect to a given data set. Hence they can detect outliers – observations that appear extreme relative to the rest of the observations. Of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tractability. In this article, we propose a novel statistical depth, the kernelized spatial depth (KSD), which generalizes the spatial depth via positive definite kernels. By choosing a proper kernel, the KSD can capture the local structure of a data set while the spatial depth fails. We demonstrate this by the half-moon data and the ring-shaped data. Based on the KSD, we propose a novel outlier detection algorithm, by which an observation with a depth value less than a threshold is declared as an outlier. The proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. It applies to a one-class learning setting, in which “normal ” observations are given as the training data, as well as to a missing label scenario where the training set consists of a mixture of normal observations and outliers with unknown labels. We give upper bounds on the false alarm probability of a depth-based detector. These upper bounds can be used to determine the threshold. We perform extensive experiments on synthetic data and data sets from real applications. The proposed outlier detector is compared with existing methods. The KSD outlier detector demonstrates competitive performance.
Semisupervised one-class support vector machines for classification of remote sensing data
- IEEE Trans. Geosci. Remote Sens
, 2010
"... Abstract—This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. In one-class image classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one ..."
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Cited by 17 (0 self)
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Abstract—This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. In one-class image classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one class are available, obtaining a reliable classifier is a difficult task. In the particular case of SVM-based classifiers, this task is even harder because the free parameters of the model need to be finely adjusted, but no clear criterion can be adopted. In order to improve the OC-SVM classifier accuracy and alleviate the problem of free-parameter se-lection, the information provided by unlabeled samples present in the scene can be used. In this paper, we present two state-of-the-art algorithms for semisupervised one-class classification for remote sensing classification problems. The first proposed algorithm is based on modifying the OC-SVM kernel by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples. The second one is based on a simple modification of the standard SVM cost function which penalizes more the errors made when classifying samples of the target class. The good performance of the proposed methods is illustrated in four challenging remote sensing image classification scenarios where the goal is to detect one of the classes present on the scene. In particular, we present results for multisource ur-ban monitoring, hyperspectral crop detection, multispectral cloud screening, and change-detection problems. Experimental results show the suitability of the proposed techniques, particularly in cases with few or poorly representative labeled samples. Index Terms—Change detection, one-class classification, one-class support vector machine (OC-SVM), semisupervised learn-ing (SSL), support vector domain description (SVDD), target detection. I.
Anomaly detection and reconstruction from random projections
- IEEE Transactions on Image Processing
"... Abstract—Compressed-sensing methodology typically employs random projections simultaneously with signal acquisition to ac-complish dimensionality reduction within a sensor device. The ef-fect of such random projections on the preservation of anomalous data is investigated. The popular RX anomaly det ..."
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Cited by 8 (2 self)
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Abstract—Compressed-sensing methodology typically employs random projections simultaneously with signal acquisition to ac-complish dimensionality reduction within a sensor device. The ef-fect of such random projections on the preservation of anomalous data is investigated. The popular RX anomaly detector is derived for the case in which global anomalies are to be identified directly in the random-projection domain, and it is determined via both random simulation, as well as empirical observation that strongly anomalous vectors are likely to be identifiable by the projection-do-main RX detector even in low-dimensional projections. Finally, a reconstruction procedure for hyperspectral imagery is developed wherein projection-domain anomaly detection is employed to par-tition the data set, permitting anomaly and normal pixel classes to be separately reconstructed in order to improve the representation of the anomaly pixels. Index Terms—Anomaly detection, compressed sensing (CS), hy-perspectral data, principal component analysis (PCA). I.
Efficient kernel orthonormalized PLS for remote sensing applications
- IEEE Transactions on Geoscience and Remote Sensing
"... This paper studies the performance and applicability of a novel Kernel Partial Least Squares (KPLS) algorithm for non-linear feature extraction in the context of remote sensing applications. The so-called Kernel Orthonomalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a ..."
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Cited by 6 (1 self)
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This paper studies the performance and applicability of a novel Kernel Partial Least Squares (KPLS) algorithm for non-linear feature extraction in the context of remote sensing applications. The so-called Kernel Orthonomalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse approximation for large scale data sets, which ultimately leads to the rKOPLS algorithm. The method is theoretically analyzed in terms of computational and memory requirements, and tested in common remote sensing applications: multiand hyperspectral image classification and biophysical parameter estimation problems. The proposed method largely outperforms the traditional (linear) PLS algorithm, and demonstrates good capabilities in terms of expressive power of the extracted non-linear features, accuracy and scalability as compared to the standard KPLS.
Investigation of nonlinearity in hyperspectral remotely sensed Imagery – a nonlinear time series analysis approach
- Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS
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Depth-Based Novelty Detection and its Application to Taxonomic Research
"... It is estimated that less than 10 percent of the world’s species have been described, yet species are being lost daily due to human destruction of natural habitats. The job of describing the earth’s remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow ..."
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Cited by 3 (2 self)
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It is estimated that less than 10 percent of the world’s species have been described, yet species are being lost daily due to human destruction of natural habitats. The job of describing the earth’s remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow pace of traditional taxonomic research. In this article, we tackle, from a novelty detection perspective, one of the most important and challenging research objectives in tax-onomy – new species identification. We propose a unique and efficient novelty detection framework based on statisti-cal depth functions. Statistical depth functions provide from the “deepest ” point a “center-outward ordering ” of multi-dimensional data. In this sense, they can detect observa-tions that appear extreme relative to the rest of the obser-vations, i.e., novelty. Of the various statistical depths, the spatial depth is especially appealing because of its compu-tational efficiency and mathematical tractability. We pro-pose a novel statistical depth, the kernelized spatial depth (KSD) that generalizes the spatial depth via positive definite kernels. By choosing a proper kernel, the KSD can cap-ture the local structure of a data set while the spatial depth fails. Observations with depth values less than a threshold are declared as novel. The proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. We give an upper bound on the false alarm probability of a depth-based detector, which can be used to determine the threshold. Experimental study demonstrates its excellent potential in new species discovery. 1.
Spatial-aware dictionary learning for hyperspectral image classication
- IEEE Transactions on Medical Imaging
, 2015
"... Abstract—This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number ..."
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Cited by 2 (0 self)
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Abstract—This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of each contextual group. The sparse coefficients are then used for classification using a linear SVM. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated mul-tispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral-resolution samples. Index Terms—Classification, hyperspectral imagery, dictionary learning, probabilistic joint sparse model, linear support vector machines. I.
Collaborative Representation for Hyperspectral Anomaly Detection
"... Abstract—In this paper, collaborative representation is pro-posed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neigh-borhoods, while anomalies cannot. The representation is ..."
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Cited by 1 (1 self)
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Abstract—In this paper, collaborative representation is pro-posed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neigh-borhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collab-oration of representation is reinforced by 2-norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the stability of the solution can be enhanced. The major advantage of the proposed algorithm is the capability of adaptively modeling the background even when anomalous pixels are involved. A kernel extension of the proposed approach is also studied. Experimental results indicate that our proposed detector may outperform the traditional detection methods such as the classic Reed–Xiaoli (RX) algorithm, the kernel RX algorithm, and the state-of-the-art robust principal component analysis based and sparse-representation-based anomaly detectors, with low compu-tational cost. Index Terms—Anomaly detection, collaborative representation, kernel collaborative representation, hyperspectral imagery (HSI), sparse representation. I.
Advantages of the Boresight Effect in Hyperspectral Data Analysis
, 2011
"... Abstract: Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight eff ..."
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Cited by 1 (0 self)
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Abstract: Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight effect. Determination of image-orientation parameters is due to the combination of an inertial measurement system (IMU) and global position system (GPS). The different positions of the IMU, the GPS antenna and the imaging sensors cause the orientation and boresight effect. Any small change in the correction of the internal orientation affects the co-registration between images extracted from the two instruments. Correcting the boresight effect is a key and almost automatic task performed by all dual-system users to better analyze the full spectral information of a given pixel. Thus, the boresight effect is considered to be noise in the system and a problem that needs to be corrected prior to any (thematic) data analysis. We propose using the boresight effect, prior to its correction, as a tool to monitor and detect spectral phenomena that can provide additional information not present in the corrected images. The advantage of using this effect was investigated with the AISA-Dual sensor, composed of an EAGLE sensor for the
