Photogrammetry and Deep Learning

doi:10.11947/j.AGCS.2018.20170640

Abstract

Abstract: Deep learning has become popular and the mainstream in types of researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network (CNN) has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effect but needs more improvement.In semantics,conventional empirical and handcrafted methods have failed to extract the semantic information accurately and failed to produce types of “semantic thematic map” as 4D productions (DEM,DOM,DLG,DRG) of photogrammetry,which causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automated production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning; the other is fine crop classification from satellite special-temporal images based on 3D CNN.

Key words: deep learning, convolutional neural network, photogrammetry, stereo matching, thematic map

CLC Number:

P237

GONG Jianya, JI Shunping. Photogrammetry and Deep Learning[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 693-704.

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