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• A semantic-based edge preserving filtering SEPF is proposed. • An effective weight coefficient is introduced to measure the quality of features of different scales, so that more qualified features have higher confidence. • Ensemble learning effectively combines multi-scale features, and the weighted voting strategy enhances the information-rich features. • The proposed method is robust and can achieve high classification accuracy when there are only a few labeled samples are available. The imbalance between limited training samples and extreme high spectral dimensions is a challenge for hyperspectral image classification. To significantly improve the classification of hyperspectral images in the case of small samples, we proposed a novel hyperspectral image classification method based on semantic filtering and ensemble learning. Semantic filtering synergistically combines the efficiency of the recursive filter and the effectiveness of the recent edge detector for scale-aware edge-preserving filtering, which can efficiently extract subjectively meaningful structures from natural scenes containing multiple-scale objects. Ensemble learning is used to fuse multi-scale features and to enhance the information-rich features. Experiments on three data sets prove that both semantic information and ensemble learning can improve classification accuracy, and their combination obtained the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Multiple kernel learning (MKL) is a popular and effective method for hyperspectral image classification. However, the communication and interaction among multiple basic kernels are insufficient among multiple basic kernels during the whole training process for traditional MKL methods. In this paper, a multiple kernel learning framework based on self-learning and mutual learning (MKML) is proposed. First, each basic kernel starts pretraining with its own training samples and then uses a trained model to make predictions. Second, the basic kernel selects some informative unlabeled samples with high entropy and queries other basic kernels for labeling. All basic kernels except the one that raised the problem negotiate together to determine the class label of the unlabeled samples. Third, the new pseudo-labeled samples are added to the initial training sample sets to train the model again. Finally, all basic kernels are combined to obtain excellent classification performance by the voting mechanism. The methodology is validated on three real hyperspectral images. The experimental results show that the proposed method exhibits better classification performance than well-known MKL methods. [ABSTRACT FROM AUTHOR]

The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove “salt-and-pepper” noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small. [ABSTRACT FROM AUTHOR]