Joint sparse model-based discriminative K-SVD for hyperspectral image classification
نویسندگان
چکیده
منابع مشابه
Joint sparse model-based discriminative K-SVD for hyperspectral image classification
Sparse representation classification (SRC) is being widely investigated on hyperspectral images (HSI). For SRC methods to achieve high classification performance, not only is the development of sparse representation models essential, the designing and learning of quality dictionaries also plays an important role. That is, a redundant dictionary with well-designated atoms is required in order to...
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Sparse representation has been extensively investigated for hyperspectral image (HSI) classification and led to substantial improvements in the performance over the traditional methods, such as support vector machine (SVM). However, the existing sparsity-based classification methods typically assume Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in...
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The ability of recording the high resolution spectral signature of earth surface would be the most important feature of hyperspectral sensors. On the other hand, classification of hyperspectral imagery is known as one of the methods to extracting information from these remote sensing data sources. Despite the high potential of hyperspectral images in the information content point of view, there...
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ژورنال
عنوان ژورنال: Signal Processing
سال: 2017
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2016.10.022