Deep learning remote sensing image rural highway sandy road section extraction method

The invention discloses a deep learning remote sensing image rural highway sandy road section extraction method, which comprises the following steps of S100, selecting a remote sensing image which isacquired by a satellite and of which the resolution is within 1m, and taking a high-resolution image...

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Bibliographic Details
Main Authors YUAN SHENGGU, SHENG GUANGXIAO, LI XIN, WEI CHEN, XU HAO, LUO LUN, YANG KE
Format Patent
LanguageChinese
English
Published 05.06.2020
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Summary:The invention discloses a deep learning remote sensing image rural highway sandy road section extraction method, which comprises the following steps of S100, selecting a remote sensing image which isacquired by a satellite and of which the resolution is within 1m, and taking a high-resolution image obtained after preprocessing as a data source; s200, constructing a network structure and a loss function extracted from a 'smooth return and unsmooth' road section; s300, training the 'smooth return and unsmooth' road segment extraction network by utilizing the training set, and repeatedly adjusting network parameters of the 'smooth return and unsmooth' road segment extraction network; s400, inputting the test set into the trained smooth-return unsmooth network, extracting a smooth-return unsmooth road section to verify and call back the network to achieve a better experimental result, and storing the network; and S500, inputting a remote sensing image acquired by a satellite into a network input end to identify a
Bibliography:Application Number: CN202010020686