Mask R-CNN-based intelligent identification of sparse woods from remote sensing images
DIAO Mingguang1(), LIU Yong1, GUO Ningbo1, LI Wenji2, JIANG Jikang1, WANG Yunxiao1
1. School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China 2. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
There are only a few low-accuracy methods available for the feature extraction of sparse woods from remote sensing images. Moreover, there is a lack of datasets for intelligent identification. This study proposed a method for intelligent information identification of sparse woods from remote sensing images. First, a dataset was created using QGIS and Python separately to provide data support for model training. Then, feature maps were generated through feature extraction, and then regions of interest (ROIs) were extracted from the feature maps. Subsequently, these ROIs were filtered through pooling operations (ROI align) to reduce the memory consumption caused by too many ROIs in the images. Experiments show that the method proposed in this study can create datasets quickly and facilitate the identification of sparse woods from remote sensing images. Moreover, the Mask R-CNN-based intelligent identification has a target detection mean average precision (MAP) of up to 0.92.
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2023, Vol. 35 
), 刘勇1, 郭宁博1, 李文吉2, 江继康1, 王云霄1
