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  • 1
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2019-02, Vol.57 (2), p.1155-1167
    Description: Scene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches.
    Subject(s): Deep learning ; recurrent neural networks (RNN) ; Image analysis ; Recurrent neural networks ; convolutional neural network (CNN) ; Attention ; long short-term memory (LSTM) ; Feature extraction ; Convolutional neural networks ; scene classification ; Remote sensing ; Saliency detection ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 2
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-06, Vol.54 (6), p.3235-3247
    Description: In this paper, we propose a novel multiple kernel learning (MKL) framework to incorporate both spectral and spatial features for hyperspectral image classification, which is called multiple-structure-element nonlinear MKL (MultiSE-NMKL). In the proposed framework, multiple structure elements (MultiSEs) are employed to generate extended morphological profiles (EMPs) to present spatial-spectral information. In order to better mine interscale and interstructure similarity among EMPs, a nonlinear MKL (NMKL) is introduced to learn an optimal combined kernel from the predefined linear base kernels. We integrate this NMKL with support vector machines (SVMs) and reduce the min-max problem to a simple minimization problem. The optimal weight for each kernel matrix is then solved by a projection-based gradient descent algorithm. The advantages of using nonlinear combination of base kernels and multiSE-based EMP are that similarity information generated from the nonlinear interaction of different kernels is fully exploited, and the discriminability of the classes of interest is deeply enhanced. Experiments are conducted on three real hyperspectral data sets. The experimental results show that the proposed method achieves better performance for hyperspectral image classification, compared with several state-of-the-art algorithms. The MultiSE EMPs can provide much higher classification accuracy than using a single-SE EMP.
    Subject(s): Support vector machines ; Classification ; extended morphological profile (EMP) ; Data mining ; Kernel ; hyperspectral images ; Hyperspectral imaging ; Principal component analysis ; multiple kernel learning (MKL) ; Principal components analysis ; Usage ; Research ; Kernel functions ; Image processing ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 3
    Language: English
    In: IEEE transactions on image processing, 2019-04, Vol.28 (4), p.1923-1938
    Description: Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented LMM, to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity and atmospheric effects) and instrumental configurations (e.g., sensor noise), and material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low-coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with the previous state-of-the-art methods.
    Subject(s): low-coherent dictionary learning ; Alternating direction method of multipliers ; Dictionaries ; spectral unmixing ; Atmospheric modeling ; Perturbation methods ; Machine learning ; spectral variability ; remote sensing ; Optimization ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 1057-7149
    E-ISSN: 1941-0042
    Source: IEEE Electronic Library (IEL)
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 4
    Language: English
    In: IEEE transactions on image processing, 2019-06, Vol.28 (6), p.3034-3047
    Description: This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t - product)-based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and the LR-HSI is built using t - product, which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, alternating direction method of multipliers is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-ofthe-art HSI super-resolution methods.
    Subject(s): Dictionaries ; nonlocal patch tensor ; Fuses ; tensor dictionary learning ; tensor sparse coding ; super-resolution ; Sparse matrices ; Hyperspectral image ; Spatial resolution ; Signal resolution ; Engineering Sciences ; Signal and Image processing
    ISSN: 1057-7149
    E-ISSN: 1941-0042
    Source: IEEE Electronic Library (IEL)
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 5
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2015-05, Vol.53 (5), p.2532-2546
    Description: In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification.
    Subject(s): Training ; Accuracy ; Training data ; hyperspectral image classification ; Markov random fields (MRFs) ; Feature extraction ; rotation forests ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 6
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2018-05, Vol.56 (5), p.2919-2930
    Description: Anomaly detection plays an important role in remotely sensed hyperspectral image (HSI) processing. Recently, compressive sensing technology has been widely used in hyperspectral imaging. However, the reconstruction from compressive HSI and detection are commonly completed independently, which will reduce the processing's efficiency and accuracy. In this paper, we propose a framework for hyperspectral compressive sensing with anomaly detection which reconstruct the HSI and detect the anomalies simultaneously. In the proposed method, the HSI is composed of the background and anomaly parts in the tensor robust principal component analysis model. To characterize the low-dimensional structure of the background, a novel tensor nuclear norm is used to constrain the background tensor. As the anomaly part is formed by a few anomalous spectra, the anomaly part is assumed to be a tuber-wise sparse tensor. In addition, to enhance the separation of the background and anomaly, we minimize the sum of Mahalanobis distance of the background pixels. Experiments on four HSIs demonstrate that the proposed method outperforms several state-of-the-art methods on both reconstruction and anomaly detection accuracies.
    Subject(s): robust principal component analysis (RPCA) ; Tensile stress ; Image coding ; reconstruction ; hyperspectral images (HSIs) ; Anomaly detection ; Image reconstruction ; Compressed sensing ; Mahalanobis distance ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 7
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2015-05, Vol.53 (5), p.2565-2586
    Description: Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.
    Subject(s): Algorithm design and analysis ; component substitution (CS) ; Transforms ; quality assessment ; multiresolution analysis (MRA) ; Benchmarking ; multispectral (MS) pansharpening ; Vectors ; Indexes ; Spatial resolution ; very high-resolution optical images ; Principal component analysis ; Engineering Sciences
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 8
    Language: English
    In: IEEE transactions on image processing, 2018-07, Vol.27 (7), p.3418-3431
    Description: Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied. Its convergence, whatever the initial guess, is demonstrated in all the practical cases and the reached asymptotic value is analytically calculated. The performance is assessed both at reduced resolution and at full resolution on four data sets acquired by the IKONOS sensor and the WorldView-3 sensor. The proposed full scale approach always shows the best performance with respect to the benchmark consisting of state-of-the-art pansharpening methods.
    Subject(s): pansharpening ; data fusion ; Satellites ; Closed-form solutions ; Estimation ; full scale estimation ; remote sensing ; Iterative methods ; Spatial resolution ; Multiresolution analysis ; Engineering Sciences ; Signal and Image processing
    ISSN: 1057-7149
    E-ISSN: 1941-0042
    Source: IEEE Electronic Library (IEL)
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 9
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-03, Vol.54 (3), p.1519-1531
    Description: With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs) have shown excellent performances for classifying hyperspectral remote sensing images. In order to further improve the performance, we propose a novel ensemble approach, namely, rotation-based SVM (RoSVM), which combines SVMs and MCSs together. The basic idea of RoSVM is to generate diverse SVM classification results using random feature selection and data transformation, which can enhance both individual accuracy and diversity within the ensemble simultaneously. Two simple data transformation methods, i.e., principal component analysis and random projection, are introduced into RoSVM. An empirical study on three hyperspectral data sets demonstrates that the proposed RoSVM ensemble method outperforms the single SVM and random subspace SVM. The impacts of the parameters on the overall accuracy of RoSVM (different training sets, ensemble sizes, and numbers of features in the subset) are also investigated in this paper.
    Subject(s): Support vector machines ; Training ; hyperspectral remote sensing image ; Accuracy ; multiple classifier systems (MCSs) ; Classification ; Hyperspectral imaging ; Principal component analysis ; rotation-based ensemble ; support vector machines (SVMs) ; Learning ; Usage ; Analysis ; Principal components analysis ; Regression analysis ; Research ; Remote sensing ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 10
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2017-11, Vol.55 (11), p.6547-6565
    Description: With the rapid development of spectral imaging techniques, classification of hyperspectral images (HSIs) has attracted great attention in various applications such as land survey and resource monitoring in the field of remote sensing. A key challenge in HSI classification is how to explore effective approaches to fully use the spatial-spectral information provided by the data cube. Multiple kernel learning (MKL) has been successfully applied to HSI classification due to its capacity to handle heterogeneous fusion of both spectral and spatial features. This approach can generate an adaptive kernel as an optimally weighted sum of a few fixed kernels to model a nonlinear data structure. In this way, the difficulty of kernel selection and the limitation of a fixed kernel can be alleviated. Various MKL algorithms have been developed in recent years, such as the general MKL, the subspace MKL, the nonlinear MKL, the sparse MKL, and the ensemble MKL. The goal of this paper is to provide a systematic review of MKL methods, which have been applied to HSI classification. We also analyze and evaluate different MKL algorithms and their respective characteristics in different cases of HSI classification cases. Finally, we discuss the future direction and trends of research in this area.
    Subject(s): Support vector machines ; Neural networks ; Classification ; Data structures ; remote sensing ; heterogeneous features ; Kernel ; hyperspectral images (HSIs) ; Hyperspectral imaging ; multiple kernel learning (MKL) ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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