Superpixel Tensor Model for Spatial–Spectral Classification of Remote Sensing Images

Abstract

Nowadays, many methods of spatial–spectral classification have been developed and achieved good results for classification with high-resolution remotely sensed images, especially superpixel-based methods. However, these methods generally consider a superpixel as a group of pixels instead of one entity, ignoring the spectral–spatial entirety in the third-order RSI data cube. In order to fully exploit the third-order spectral–spatial information, in this paper, we propose a superpixel-based tensor model for RSI classification, where a multiattribute superpixel tensor (MAST) model is constructed on the top of multiattribute superpixel maps based on the concept of extended morphological profiles (EMAPs). In order to manage the adaptive spatial nature of superpixels, we develop an increment strategy to augment all superpixels with filling up their own envelop rectangles including three different ways, i.e., 0 vector, mean vector of all the pixels within the superpixel, or original pixels. Then, we use CANDECOMP/PARAFAC (CP) decomposition to obtain the features of the unified dimension from the MASTs of various sizes. Especially, CP decomposition can deal with missing data, so we also got a fourth means of constructing the MAST. Finally, base kernels calculated, respectively, from the original spectral feature, EMAP features and MAST features are learned by multiple kernel learning methods, with the optimal kernel fed to a support vector machine to complete the classification task. The experiments conducted on four real RSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.