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. Buildings, where most human activities happen, are one of the most important crucial objects in remote sensing images. Extracting building information is of great significance importance for conducting sustainable development-related researches. The extracted building information is a fundamental data source for further researches, including evaluating the living conditions of people, monitoring building conditions, predicting disaster risks and so on. In recent years, convolutional neural networks have been widely employed in building detection, and have gained significant progresses. However, in these automatic detection procedures, the critical brightness information is often neglected, with all buildings simply classified into the same category. To make the building detection more efficient and precise, we propose a simple yet efficient multitask method employing several lightness detectors, each of which is dedicated to the building detection in a specific brightness interval. Experiment results show that the building detection accuracy could be improved by 8.1% with the assistance of the additional lightness information.

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A METHOD OF BUILDING DETECTION IN REMOTE SENSING IMAGES BASED ON DEEP LEARNING WITH MULTIPLE LIGHTNESS DETECTORS