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  • 1
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2018-04, Vol.56 (4), p.1940-1958
    Description: Hyperspectral image (HSI) noise reduction is an active research topic in HSI processing due to its significance in improving the performance for object detection and classification. In this paper, we propose a joint spectral and spatial low-rank (LR) regularized method for HSI denoising, based on the assumption that the free-noise component in an observed signal can exist in latent low-dimensional structure while the noise component does not have this property. The proposed HSI denoising method not only considers the traditional LR property across the spectral domain but also leverages nonlocal LR property over the spatial domain. The main contribution of this paper is the incorporation of the low-rankness-based nonlocal similarity into sparse representation to characterize the spatial structure. Specially, the similar patches in each cluster usually contain similar sharp structure such as edges and textures; LR performed on cluster entitles to achieve a lower rank than that on the global spectral correlation. To make the proposed method more tractable and robust, we develop a variable splitting-based technique to solve the optimization problem. Experiment results on both simulated and real hyperspectral data sets demonstrate that the proposed method outperforms state-of-the-art methods with significant improvements both visually and quantitatively.
    Subject(s): Hyperspectral image (HSI) denoising ; Correlation ; Dictionaries ; Gaussian noise ; Noise reduction ; low-rank (LR) dictionary ; nonlocal self-similarity ; Spectral analysis ; spectrum correlation ; Hyperspectral imaging ; sparse representation
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 2
    Language: English
    In: IEEE geoscience and remote sensing letters, 2015-03, Vol.12 (3), p.552-556
    Description: Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height in light detection and ranging (LiDAR) data, and geometry in image processing technologies, such as morphological profiles (MPs)]. It is clear that no single technology can be sufficient for a reliable classification, but combining many of them can lead to problems such as the curse of dimensionality, excessive computation time, and so on. Applying feature reduction techniques on all the features together is not good either, because it does not take into account the differences in structure of the feature spaces. Decision fusion, on the other hand, has difficulties with modeling correlations between the different data sources. In this letter, we propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of the spectral information (of the original HSI) and MPs (built on both HS and LiDAR data). In the proposed method, the edges of the fusion graph are weighted by the distance between the stacked feature points. This yields a clear improvement over an older approach with binary edges in the fusion graph. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.
    Subject(s): hyperspectral image (HSI) ; Laser radar ; Accuracy ; Urban areas ; Feature extraction ; Data fusion ; remote sensing ; graph-based ; light detection and ranging (LiDAR) data ; Hyperspectral imaging
    ISSN: 1545-598X
    E-ISSN: 1558-0571
    Source: IEEE Electronic Library (IEL)
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  • 3
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2013-01, Vol.51 (1), p.184-198
    Description: We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods.
    Subject(s): Training ; hyperspectral remote sensing ; Laplace equations ; Classification ; semisupervised ; Feature extraction ; Educational institutions ; Eigenvalues and eigenfunctions ; Hyperspectral imaging ; Internal geophysics ; Earth, ocean, space ; Applied geophysics ; Earth sciences ; Exact sciences and technology ; Discriminant analysis ; Factor analysis ; Research ; Spectrum analysis ; Remote sensing ; Methods
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 4
    Language: English
    In: Sensors (Basel, Switzerland), 2018-05-24, Vol.18 (6), p.1695
    Description: Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, nonlocal self-similarity (NLSS) has gradually gained support since it can be used to support spatial coherence tasks. However, these methods are biased towards the direct use of spatial information as a whole, while discriminative spectral information is not well exploited. In this paper, we propose a novel method to couple both nonlocal spatial and local spectral similarity together in a single framework. In particular, the proposed approach exploits nonlocal spatial similarities by searching non-overlapped patches, whereas spectral similarity is analyzed locally within the locally discovered patches. By fusion of nonlocal and local information, we then apply group sparse representation (GSR) for classification based on a group structured prior. Experimental results on three real hyperspectral data sets demonstrate the efficiency of the proposed approach, and the improvements are significant over the methods that consider either nonlocal or local similarity.
    Subject(s): local spectral similarity ; group sparse representation (GSR) ; hyperspectral imagery classification ; nonlocal spatial similarity
    ISSN: 1424-8220
    E-ISSN: 1424-8220
    Source: Academic Search Ultimate
    Source: PubMed Central
    Source: Directory of Open Access Journals
    Source: Alma/SFX Local Collection
    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 journal of selected topics in applied earth observations and remote sensing, 2014-06, Vol.7 (6), p.2405-2418
    Description: The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.
    Subject(s): VHR imagery ; Laser radar ; Light Detection And Ranging (LiDAR) ; urban ; Data integration ; Vegetation mapping ; hyperspectral ; Feature extraction ; Data fusion ; multi-modal ; Hyperspectral imaging
    ISSN: 1939-1404
    E-ISSN: 2151-1535
    Source: IEEE Electronic Library (IEL)
    Source: Directory of Open Access Journals
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  • 6
    Language: English
    In: Sensors (Basel, Switzerland), 2018-11-05, Vol.18 (11), p.3774
    Description: Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambiguities. Based on high-level contextual features, the deep convolutional neural network (DCNN) is an effective method to deal with semantic segmentation of high-resolution aerial imagery. In this work, a novel dense pyramid network (DPN) is proposed for semantic segmentation. The network starts with group convolutions to deal with multi-sensor data in channel wise to extract feature maps of each channel separately; by doing so, more information from each channel can be preserved. This process is followed by the channel shuffle operation to enhance the representation ability of the network. Then, four densely connected convolutional blocks are utilized to both extract and take full advantage of features. The pyramid pooling module combined with two convolutional layers are set to fuse multi-resolution and multi-sensor features through an effective global scenery prior manner, producing the probability graph for each class. Moreover, the median frequency balanced focal loss is proposed to replace the standard cross entropy loss in the training phase to deal with the class imbalance problem. We evaluate the dense pyramid network on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2D semantic labeling dataset, and the results demonstrate that the proposed framework exhibits better performances, compared to the state of the art baseline.
    Subject(s): densely connected convolutions ; pyramid pooling module ; high-resolution aerial imageries ; semantic segmentation
    ISSN: 1424-8220
    E-ISSN: 1424-8220
    Source: Academic Search Ultimate
    Source: PubMed Central
    Source: Directory of Open Access Journals
    Source: Alma/SFX Local Collection
    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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  • 7
    Language: English
    In: Remote sensing (Basel, Switzerland), 2019-01-19, Vol.11 (2), p.193
    Description: Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of the available HSI-CSR methods consider the sparsity prior in spatial and spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, in most previous works, little attention has been paid to exploiting the underlying nonlocal structure in spatial domain of the HSI. In this paper, we propose a nonlocal tensor sparse and low-rank regularization (NTSRLR) approach, which can encode essential structured sparsity of an HSI and explore its advantages for HSI-CSR task. Specifically, we study how to utilize reasonably the l 1 -based sparsity of core tensor and tensor nuclear norm function as tensor sparse and low-rank regularization, respectively, to describe the nonlocal spatial-spectral correlation hidden in an HSI. To study the minimization problem of the proposed algorithm, we design a fast implementation strategy based on the alternative direction multiplier method (ADMM) technique. Experimental results on various HSI datasets verify that the proposed HSI-CSR algorithm can significantly outperform existing state-of-the-art CSR techniques for HSI recovery.
    Subject(s): hyperspectral image ; tensor sparse decomposition ; tensor low-rank approximation ; compressive sensing ; structured sparsity
    ISSN: 2072-4292
    E-ISSN: 2072-4292
    Source: Academic Search Ultimate
    Source: Directory of Open Access Journals
    Source: ProQuest Central
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  • 8
    Language: English
    In: IEEE geoscience and remote sensing letters, 2016-05, Vol.13 (5), p.686-690
    Description: Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs. SGE explores the sparsity of the HSI data and can achieve good results. However, in most cases, locality is more important than sparsity when learning the features of the data. In this letter, we propose an extended SGE method: the weighted sparse graph based DR (WSGDR) method for HSIs. WSGDR explicitly encourages the sparse coding to be local and pays more attention to those training pixels that are more similar to the test pixel in representing the test pixel. Furthermore, WSGDR can offer data-adaptive neighborhoods, which results in the proposed method being more robust to noise. The proposed method was tested on two widely used HSI data sets, and the results suggest that WSGDR obtains sparser representation results. Furthermore, the experimental results also confirm the superiority of the proposed WSGDR method over the other state-of-the-art DR methods.
    Subject(s): hyperspectral image (HSI) ; Collaboration ; Encoding ; Robustness ; sparse graph embedding (SGE) ; nearest neighbor graph ; weighted sparse coding ; Hyperspectral imaging ; Dimensionality reduction (DR)
    ISSN: 1545-598X
    E-ISSN: 1558-0571
    Source: IEEE Electronic Library (IEL)
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  • 9
    Language: English
    In: Remote sensing (Basel, Switzerland), 2017-06-01, Vol.9 (6), p.548
    ISSN: 2072-4292
    E-ISSN: 2072-4292
    Source: Academic Search Ultimate
    Source: Directory of Open Access Journals
    Source: ProQuest Central
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  • 10
    Language: English
    In: Remote sensing (Basel, Switzerland), 2019-03-14, Vol.11 (6), p.624
    Description: Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple feature sets and decisions from individual classifiers to be employed in a joint manner. In this work, we present a flexible decision fusion framework addressing these issues. In a first step, we propose to use sparse fractional abundances as decision source, complementary to class probabilities obtained from a supervised classifier. This specific selection of complementary decision sources enables the description of a pixel in a more complete way, and is expected to mitigate the effects of small training samples sizes. Secondly, we propose to apply a fusion scheme, based on the probabilistic graphical Markov Random Field (MRF) and Conditional Random Field (CRF) models, which inherently employ spatial information into the fusion process. To strengthen the decision fusion process, consistency links across the different decision sources are incorporated to encourage agreement between their decisions. The proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward way. Experimental results conducted on two real hyperspectral images show superiority over several other approaches in terms of classification performance when very limited training data is available.
    Subject(s): conditional random field ; hyperspectral unmixing ; Markov random field ; decision fusion ; supervised classification
    ISSN: 2072-4292
    E-ISSN: 2072-4292
    Source: Academic Search Ultimate
    Source: Directory of Open Access Journals
    Source: ProQuest Central
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