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The spectral–spatial classification methods have improved the classification accuracy of hyperspectral imagery dramatically, however, the traditional random sampling method causes the high overlap between training samples and testing samples. So, the improvement of the accuracy is partly due to the overlap of these samples. In this paper, a non-overlapping classification framework for hyperspectral imagery based on the distance of regularized nearest points (NORNP) is proposed. First, a novel sampling method named controlled random sampling is adopted to generate the training sets. The controlled random sampling can reduce the overlap between the training and testing samples a lot. The training samples with the same label will be used to form a set. Then, the superpixel segmentation method is adopted to divide the hyperspectral imagery into many smoothed small regions. Finally, all sets are represented as regularized affine hulls (RAHs). Every two RAHs can automatically choose two regularized nearest points (RNP) by an iterative algorithm. The distance between RNPs is used as the sets distance. The label of testing set is determined by the minimum distance to each training set. Experimental results based on three real hyperspectral data sets demonstrate that the proposed set-to-set classification method can provide better classification results when training samples and testing samples are less overlapped. • Non-overlapping sampling is used for the classification of Hyperspectral Imagery. • Non-overlapping classification with regularized nearest points distance is proposed. • Removes the influence of the overlap between training and testing samples. • Classification accuracy of NORNP is compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the introduction of spatial-spectral fusion and deep learning, the classification performance of hyperspectral imagery (HSI) has been promoted greatly. For some widely used datasets, the classification accuracy almost reaches 100%. However, for hyperspectral image classification, random sampling is still the most common strategy to collect the training and test samples. Because the training and test samples are randomly selected from the same image, so they have a high correlation and the classification results are overoptimistic. Besides, random sampling is not a good choice for practical applications because we cannot always collect training and test samples from the same region. Disjoint sampling selects training and testing samples from different local regions, which will provide a more objective performance evaluation for HSI classification models. In this paper, we first show the huge classification performance difference caused by different sampling strategies with a simple experiment, then we analyze the underlying reasons from the spectral information, spatial-spectral combination, sample overlapping and spatial distance, finally, a semi-supervised feature learning method is proposed for disjoint HSI classification, in which the spatial and spectral information are exploited effectively and reasonably. The experimental results based on three HSI datasets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Ensemble learning can improve the performance of classification by integrating a set of classifiers, and shows significant potential benefits to the classification of hyperspectral image. However, the ensemble strategy remarkably influences the classification results, which include determining the minimum number of classifiers and assigning advisable weights associated with each classifier. In this paper, we present a novel sparse ensemble learning method with spectral–spatial knowledge for hyperspectral image classification. It considers the ensemble strategy under sparse recovery framework, where the solved non-zero coefficients reveal the importance of the selected classifier, from which a compact and effective ensemble learning system can be derived. Moreover, the spatial information is incorporated into the classification to develop a spectral–spatial joint sparse representation based ensemble learning algorithm for more accurate classification of hyperspectral images. Experimental results on several real hyperspectral images show that the proposed sparse ensemble system can achieve better performance than traditional ensemble learning methods using all classifiers, and it largely improves the efficiency in testing phase. [ABSTRACT FROM AUTHOR]

Spectral–spatial classification methods can improve the classification accuracy of hyperspectral imagery (HSI) dramatically. However, this improvement is caused in part by the overlap between training set and test set. In this paper, a novel non-overlapping spectral-spatial classification framework based on set-to-sets distance is proposed. First, each class is sampled using a controlled random sampling method, which is regarded as a training set. The image is segmented into many superpixels and each superpixel is taken as a test set. In order to ensure that the test set and the training set are not overlapped with each other, the training pixels contained in each test superpixel are deleted. Then, the training set of each class is compressed into a more compact set to reduce the computational complexity and kernel trick is used to make samples be approximately linearly separable. Finally, each test set is modeled as a convex hull, this hull is represented collaboratively with all training sets. With the resolved representation coefficients, the distance between the test set and each training set can be calculated for classification. Experimental results based on three real HSI data sets demonstrate the superiority of the proposed method to state-of-the-art algorithms under the non-overlapping sampling strategy. [ABSTRACT FROM AUTHOR]

The high dimensionality of hyperspectral imagery often introduces challenge for the conventional data analysis techniques. In order to improve the classification performance of hyperspectral imagery, metric learning is often introduced to assign small distances between samples from the same class and large distances from different class. However, most of the traditional metric learning methods only adopt linear transformations, which cannot capture the complex nonlinear relationships between high dimensional samples. Inspired by the successful application of deep learning for the classification of hyperspectral imagery. In this paper, the deep neural network is introduced to learn the discriminating metric for the classification of hyperspectral images. In order to improve the reliability of classification results, the spectral and spatial information are combined with weighted classification probabilities. Experimental results demonstrate that the proposed method achieves satisfactory classification performance when compared with other metric learning methods or deep models. [ABSTRACT FROM AUTHOR]