Abstract
In this paper, we propose a new efficient superpixel-based feature extraction and fusion method on hyperspectral and LiDAR data. Such important factor that the adjacent pixels belong to the same class with high probability is taken into consideration in our method, which means each superpixel can be regarded as a small region consisting of a number of pixels with similar spectral characteristics. In order to represent each superpixel well, we use our Gabor-wavelet-based feature extraction approach instead of morphological APs. A feature selection and fusion process has also been used to reduce the redundancy among Gabor features and make the fused feature more discriminative. The results on the several real dataset indicate that the proposed method provides state-of-the-art classification results, respectively, even when only few samples, i.e., only three samples per class, are labeled.