A Novel Center-Boundary Metric Loss to Learn Discriminative Features for Hyperspectral Image Classification

Learning discriminative features is of crucial for hyperspectral image (HSI) classification. Though metric learning has been applied to learn effective features in HSI classification tasks, existing metric loss functions only consider distance among features of sample pairs but ignore the feature centers and boundaries in the embedding feature space, which limits the discrimination of learned features. In this paper, a novel metric loss function named center-boundary metric loss (CBML) is proposed to learn more discriminative features so as to improve HSI classification performance. Unlike the existing metric loss functions, CBML not only considers the distance between sample pairs to enhance intra-class similarity and inter-class separability but also pays more attention to the feature centers and boundaries in the embedding feature space that could greatly determine and affect the category of features. Specifically, CBML forces the distance of a sample to its corresponding feature center to be explicitly smaller than that to samples from other classes by a predefined threshold. As a result, the boundaries of different classes will separate an actual distance, which improves the discrimination of learned features. Moreover, in order to improve the training efficiency, a cross mini-batch sampling strategy is further proposed to break through the limitation within the mini-batch by using features between several contiguous mini-batches to sample pairs without increasing the size of the mini-batch. Accordingly, the sampling range of sample pairs is greatly expanded, and the training data is more fully exploited. Experimental results over four benchmark datasets with a typical network for HSI classification demonstrate our proposed method outperforms several state-of-the-arts.