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Motivated by the state-of-the-art optical sensing and image processing technologies, remote urban sensing (RUS) has emerged as a powerful sensing paradigm to capture abundant visual information about the urban environment for intelligent city monitoring, planning, and management. In this article, we focus on a classification and super-resolution coupling (CSC) problem in RUS applications, where the goal is to explore the interdependence between two critical tasks (i.e., <italic>classification</italic> and <italic>super-resolution</italic>) to concurrently boost the performance of both the tasks. Two fundamental challenges exist in solving our problem: 1) it is challenging to obtain accurate classification results and generate high-quality reconstructed images without knowing either of them <italic>a priori</italic> and 2) the noise embedded in the image data could be amplified infinitely by the complex interdependence and coupling between the two tasks. To address these challenges, we develop <italic>SCLearn</italic>, a novel deep convolutional neural network architecture, to couple the classification task with the super-resolution task in an integrated learning framework to <italic>concurrently</italic> boost the performance of both the tasks. The evaluation results on a real-world RUS application over two different cities in Europe (Barcelona and Berlin) show that SCLearn consistently outperforms the state-of-the-art baselines by simultaneously achieving better land usage classification accuracy and higher reconstructed image quality under various application scenarios.

On Coupling Classification and Super-Resolution in Remote Urban Sensing: An Integrated Deep Learning Approach