A Coarse-to-Fine Cell Division Approach for Hyperspectral Remote Sensing Image Classification

CNNs are widely used in remote sensing image classification because of its outstanding feature extraction ability. However, the classification performance is limited by the complexity of remote sensing scenes and the large inter-class similarity. Furthermore, the existing methods usually distinguish multiple classes of complex targets at the same time, which brings great difficulties to the classification model. To alleviate the above problems, we propose a coarse-to-fine cell division (CFCD) approach to improve HRSIs classification. The algorithm divides the limited labeled samples into two subclasses through continuous decomposition, which reduces the similarity between the ground object classes from the data level. We employ the
$\ell _{12}$
-norm to depict the specific distribution of the target for only two subclasses rather than multiple classes of ground objects, so that the exclusive features of targets can be selected more accurately. Moreover, we propose an optimization process of multi-level training, which not only significantly reduces the difficulty of distinguishing multi-class targets, but also improves the utilization of training samples. Experimental results show that the CFCD algorithm outperforms the state-of-the-art methods with limited training samples on three publicly available HRSIs datasets.