Hyperspectral Image Classification With Deep Metric Learning and Conditional Random Field

To improve the classification performance in the context of hyperspectral image (HSI) processing, many works have been developed based on two common strategies, namely, the spatial–spectral information integration and the utilization of neural networks. However, both strategies typically require more training data than the classical algorithms, aggregating the shortage of labeled samples. In this letter, we propose a novel framework that organically combines the spectrum-based deep metric learning (DML) model and the conditional random field (CRF) algorithm. The DML model is supervised by the center loss to produce spectrum-based features that gather more tightly in Euclidean space within classes. The CRF with Gaussian edge potentials, which is first proposed for image segmentation tasks, is introduced to give the pixel-wise classification over the HSI by utilizing both the geographical distances between pixels and the Euclidean distances between the features produced by the DML model. The proposed framework is trained by spectral pixels at the DML stage and utilizes the half handcrafted spatial features at the CRF stage. This settlement alleviates the shortage of training data to some extent. Experiments on two real HSIs demonstrate the advantages of the proposed method in terms of both classification accuracy and computation cost.