Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles

Signal & Image Dept., Grenoble Inst. of Technol., Grenoble
IEEE Transactions on Geoscience and Remote Sensing (Impact Factor: 3.51). 12/2008; 46(11):3804 - 3814. DOI: 10.1109/TGRS.2008.922034
Source: OAI

ABSTRACT A method is proposed for the classification of urban hyperspectral data with high spatial resolution. The approach is an extension of previous approaches and uses both the spatial and spectral information for classification. One previous approach is based on using several principal components (PCs) from the hyperspectral data and building several morphological profiles (MPs). These profiles can be used all together in one extended MP. A shortcoming of that approach is that it was primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, the commonly used pixelwise classification of hyperspectral data is solely based on the spectral content and lacks information on the structure of the features in the image. The proposed method overcomes these problems and is based on the fusion of the morphological information and the original hyperspectral data, i.e., the two vectors of attributes are concatenated into one feature vector. After a reduction of the dimensionality, the final classification is achieved by using a support vector machine classifier. The proposed approach is tested in experiments on ROSIS data from urban areas. Significant improvements are achieved in terms of accuracies when compared to results obtained for approaches based on the use of MPs based on PCs only and conventional spectral classification. For instance, with one data set, the overall accuracy is increased from 79% to 83% without any feature reduction and to 87% with feature reduction. The proposed approach also shows excellent results with a limited training set.

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Available from: Jocelyn Chanussot, Sep 27, 2015
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    • "In addition to the CK-or MRF-based framework, many researchers have worked with other spatial features. For instance, morphological profile (MP) generated by certain morphological operators (e.g., opening and closing), which is widely used for modeling structural information, was introduced in [10]. In [11], a spectral–spatial preprocessing method was proposed for noise-robust HSI classification by employing a multihypothesis prediction strategy which was originally developed for compressed sensing image reconstruction [12] and superresolution [13]. "
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    ABSTRACT: It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.
    IEEE Transactions on Geoscience and Remote Sensing 06/2015; 53(7):1-13. DOI:10.1109/TGRS.2014.2381602 · 3.51 Impact Factor
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    • "In recent years, spatial information has been taken into account and some spectral–spatial-based classifiers have been proposed, and these methods provided significant advantages in terms of improving performance [23], [24]. In [25], the proposed method was based on the fusion of morphological information and original data followed by SVM. In [26], a new classification framework was proposed to exploit the spatial and spectral information using loopy belief propagation and active learning. "
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    ABSTRACT: Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral–spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with hyperspectral data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for hyperspectral data classification.
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 06/2015; 8(6):1-12. DOI:10.1109/JSTARS.2015.2388577 · 3.03 Impact Factor
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    • "However, these operators make changes on the shape of the structures which are still present in the image after the opening/closing. Therefore, they can introduce fake objects in the image [21]. One way to solve this issue is to consider opening and closing by reconstruction. "
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    ABSTRACT: A robust framework for the classification of hyperspectral images which takes into account both spectral and spatial information is proposed. The extended multivariate attribute profile (EMAP) is used for extracting spatial information. Moreover, for solving the so-called curse of dimensionality, supervised feature extraction is carried out on both the original hyperspectral data and the output of the EMAP. After performing the dimensionality reduction, two output vectors of the original data and attributes are concatenated into one stacked vector. The final classification map is achieved by using a random-forest classifier. The main difficulties of using an EMAP is to initialize the attribute parameters. Therefore, a fully automatic scheme of the proposed method is introduced to overcome the shortcomings of using EMAP. The proposed method is tested on two widely known data sets. Experimental results confirm that the proposed method provides an accurate classification map in an acceptable CPU processing time.
    IEEE Transactions on Geoscience and Remote Sensing 09/2014; 52(9):5771-5782. DOI:10.1109/TGRS.2013.2292544 · 3.51 Impact Factor
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