A SPECTRAL-SPATIAL AUGMENTED ACTIVE LEARNING METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Keywords: Hyperspectral Image Classification, Active Learning, Kernel Minimum Noise Fraction, Shearlet Transform, Data Augmentation, Outlier Elimination, Kolmogorov-Smirnov Test
Abstract. In this paper, a new classification technique for hyperspectral images (HSIs) based on an augmented active learning (AL) is introduced. The proposed method consists of two main steps: first, a 2-D non-subsampled shearlet transform (NSST) is applied to each spectral band of HSIs to extract the spatial features. After that, the kernel minimum noise fraction (KMNF) is used to reduce the spectral dimension. Second, the classification task using an augmented active learning technique is performed. For this purpose, an iterative process is considered. At each iteration, a discriminative sample selection and augmentation are used to create the training set. Then, the support vector machine (SVM) is iteratively applied to the training set. In the proposed method, the most informative samples are selected by a new query function combination of a posterior probability-based uncertainty and angle-based diversity criteria. The augmentation strategy during the training process is chosen by two-sample Kolmogorov-Smirnov test and the existing outliers are removed by k-means clustering. Finally, the proposed algorithm is applied to the real datasets and compared with three state-of-the-art AL algorithms. The obtained results show that the proposed method significantly increases accuracy considering the most informative samples.