Joint Deep Learning: A novel framework for urban land cover and land use classification

Zhang, Ce and Pan, Xin and Li, Huapeng and Zhang, Shuqing and Atkinson, Peter (2020) Joint Deep Learning: A novel framework for urban land cover and land use classification. In: GISRUK 2020, 2020-07-21 - 2020-07-23.

Abstract

The majority of existing research on urban land cover and land use (LULC) classification from remotely sensed imagery is to differentiate at a specific level (either land cover (LC) or land use (LU)) and at a specific scale. The spatial and hierarchical relationships between LC and LU were not considered in the previous paradigm. This paper proposed Joint Deep Learning as a novel framework for urban LULC classification. Three state-of-the-art methods were compared under the framework, including JDL, SS-JDL and a newly proposed Cross GAN. Experimental results demonstrate the superiority of Cross GAN in terms of classification accuracy and robustness.