Instance segmentation in aerial images is of great significance for remote sensing applications, and it is inherently more challenging because of cluttered background, extremely dense and small objects, and objects with arbitrary orientations. Besides, current mainstream CNN-based methods often suffer from the trade-off between labeling cost and performance. To address these problems, we present a pipeline of hybrid supervision. In the pipeline, we design an ancillary segmentation model with the bounding box attention module and bounding box filter module. It is able to generate accurate pseudo pixel-wise labels from real-world aerial images for training any instance segmentation models. Specifically, bounding box attention module can effectively suppress the noise in cluttered background and improve the capability of segmenting small objects. Bounding box filter module works as a filter which removes the false positives caused by cluttered background and densely distributed objects. Our ancillary segmentation model can locate object pixel-wisely instead of relying on horizontal bounding box prediction, which has better adaptability to arbitrary oriented objects. Furthermore, oriented bounding box labels are utilized for handling arbitrary oriented objects. Experiments on iSAID dataset show that the proposed method can achieve comparable performance (32.1 AP) to fully supervised methods (33.9 AP), which is obviously higher than weakly supervised setting (26.5 AP), when using only 10% pixel-wise labels.

The multilevel tiled map service is widely used and serves as a kind of digital infrastructure. These map tiles are usually rendered from vector data, whose update needs to walk or drive with professional equipment to check every point of interest. This leads to inconvenience and expensive cost in timely updating maps. Compared with vector data, aerial images are much easier and cheaper to obtain. In this article, we propose a novel multilevel map (MLM) generation framework that can automatically generate accurate and consistent maps with multiple zoom levels from aerial images. It consists of a level-aware map generator and a consistency-aware map generator. The level-aware map generator is able to generate accurate initial maps with realistic details for each zoom level. The consistency-aware map generator regards the initial maps at each zoom level as a sequence and builds the connection between them, so as to guarantee content consistency between maps at different zoom levels. Furthermore, we collect a large-scale high-quality dataset called MLM for map generation at multiple zoom levels. Experiments on our MLM dataset show that our method outperforms the previous state-of-the-art map generation methods on both comprehensive quantitative metrics and perceptual quality.

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