Beyond pixels: Learning from multimodal hyperspectral superpixels for land cover classification

Despite tons of advanced classification models that have recently been developed for the land cover mapping task, the monotonicity of a single remote sensing data source, such as only using hyperspectral data or multispectral data, hinders the classification accuracy from being further improved and tends to meet the performance bottleneck. For this reason, we develop a novel superpixel-based subspace learning model, called Supace, by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC results. Supace can learn a common subspace across multimodal RS data, where the diverse and complementary information from different modalities can be better combined, being capable of enhancing the discriminative ability of to-be-learned features in a more effective way. To better capture semantic information of objects in the feature learning process, superpixels that beyond pixels are regarded as the study object in our Supace for LCC. Extensive experiments have been conducted on two popular hyperspectral and multispectral datasets, demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.

Automated summary

This study develops a novel superpixel-based subspace learning model that can learn more accurate land cover classification results from multimodal remote sensing data.