Acta Optica Sinica, Volume. 39, Issue 12, 1228002(2019)
Remote Sensing Building Detection Based on Binarized Semantic Segmentation
To address the problem of high resource consumption and difficulty of hardware transplantation involved in utilizing deep convolutional networks for real-time detection of remote sensing building, a semantic segmentation network based on the mixed method of binary and floating-point parameters, i.e., mixed binary U-shape network (MBU-Net), is proposed. To compress the model size, the weights of a float U-shape network (FU-Net) are binarized. The output layer weights that account for a small number of parameters are replaced by floating-point type parameters to resolve the poor detection accuracy and low training speed in a global binary network. Experiments using the QuickBird satellite remote sensing dataset show that the pixel accuracy of MBU-Net is 82.33% and the harmonic average of the recall rate and accuracy rate (F1 score ) is 73.15%. Compared with the FU-Net,the MBU-Net can ensure the detection accuracy. The size of model is greatly compressed, the detection speed is increased by 6.29 times, and the power consumption is reduced to 37.78%, further demonstrating that the MBU-Net is superior to other similar methods (Deeplab and ENet). This finding has important practical engineering value for the real-time detection of remote sensing buildings.
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Tianyou Zhu, Feng Dong, Huixing Gong. Remote Sensing Building Detection Based on Binarized Semantic Segmentation[J]. Acta Optica Sinica, 2019, 39(12): 1228002
Category: Remote Sensing and Sensors
Received: May. 27, 2019
Accepted: Aug. 13, 2019
Published Online: Dec. 6, 2019
The Author Email: Gong Huixing (hxgong@mail.sitp.ac.cn)