Dense Greenhouse Extraction in High Spatial Resolution Remote Sensing Imagery

Abstract

Greenhouses are densely distributed across the cultivated land in high-resolution remote sensing imagery, resulting in the problem of dense object extraction. On the one hand, objects tend to be wrongly merged into one object since objects are connected; on the other hand, the existed random sampling scheme does not make good use of the object distribution density to improve the training effect. To meet the demand for dense greenhouse extraction, this paper proposes a novel deep learning-based greenhouse extraction algorithm. To solve the problem that dense objects tend to be wrongly merged, this paper proposes a dual-task learning module, which uses the discriminant object boundary of the pixel-based branch to distinguish the objects of the object-based branch; to take advantage of the object distribution density for effective training, a high-density biased sampler is proposed. Moreover, this paper provides a dataset of manually labeled imagery to train and develop the proposed method. Results on greenhouse extraction in six regions in China achieve peak mIoU and mAP value, surpassing the state-of-the-art methods. Finally, a product of a greenhouse map in China is provided for analysis.