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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]

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]