MULTITASK CLASSIFICATION OF REMOTE SENSING SCENES USING DEEP NEURAL NETWORKS

The problem of scene classification in remote sensing (RS) images has attracted a lot of attention recently. Many datasets have been presented in the literature for this purpose with each claiming to be the benchmark dataset. In this paper, we propose a different approach to the RS community. Instead of putting our effort in building larger and large scene datasets, we argue that it is better to build a machine learning framework that can learn from all available datasets. We formulate this as a multitask learning problem where each dataset represents a task. Then, we present a deep learning solution that can perform multitask learning. We test the proposed multitask network on three popular scene datasets, namely UC Merced, KSA, and AID datasets. Preliminary results show the promising capabilities of this solution at sharing information between tasks and improving the classification accuracy.