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所属单位：信息科学技术学院

发表刊物：2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

项目来源：其他课题

摘要：In this paper, we focus on ROI extraction with only scribble annotations for remote sensing images (RSIs). The main challenges exist in predicting precise region boundary and suppressing complex background interference in RSIs. To address these issue, we propose a scribble-supervised residual dense dilated network (RD-DN) trained with a novel label update strategy to produce integral ROIs with accurate boundary. Specifically, the hybrid dilated convolution block is introduced as the basic module of the RD-DN, aiming to help provide much denser results. The RD-DN is first trained with only scribble labels to generate initial results. Then, as training phase goes, the label update strategy updates the labels iteratively by combining the initial results and the scribble annotations with a morphological dilation procedure. Comprehensive experiments on the GeoEye-1 dataset demonstrate the superiority of our proposal compared with state-of-the-art ROIs extraction methods.

论文类型：论文集

卷号：1

是否译文：否

发表时间：2021-01-07

第一作者：马洁