Deep learning satellite data cloud detection method supported by hyperspectral data
The invention discloses a deep learning satellite data cloud detection method supported by hyperspectral data. The method comprises the following steps: selecting enough cloud and clear sky pixels toconstruct a hyperspectral data sample library, and performing analog computation on the hyperspectral...
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creator | WANG CHUNXIANG SUN LIN JIA SHANGFENG |
description | The invention discloses a deep learning satellite data cloud detection method supported by hyperspectral data. The method comprises the following steps: selecting enough cloud and clear sky pixels toconstruct a hyperspectral data sample library, and performing analog computation on the hyperspectral pixel sample library according to parameters such as a spectral response function and a waveband width of a to-be-detected sensor to obtain a cloud and clear sky surface pixel library of the to-be-detected sensor; based on a Keras deep learning framework, designing a deep BP neural network for cloud detection, inputting multispectral sample data obtained through simulation into the network for training and learning, and obtaining a multispectral sensor cloud detection rule based on spectral characteristics; based on a Markov random field model, optimizing a cloud detection result by utilizing an iterative condition mode algorithm, and removing a part of misclassification and misclassification errors of cloud detec |
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The method comprises the following steps: selecting enough cloud and clear sky pixels toconstruct a hyperspectral data sample library, and performing analog computation on the hyperspectral pixel sample library according to parameters such as a spectral response function and a waveband width of a to-be-detected sensor to obtain a cloud and clear sky surface pixel library of the to-be-detected sensor; based on a Keras deep learning framework, designing a deep BP neural network for cloud detection, inputting multispectral sample data obtained through simulation into the network for training and learning, and obtaining a multispectral sensor cloud detection rule based on spectral characteristics; based on a Markov random field model, optimizing a cloud detection result by utilizing an iterative condition mode algorithm, and removing a part of misclassification and misclassification errors of cloud detec</description><language>chi ; eng</language><subject>CALCULATING ; COLORIMETRY ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; HANDLING RECORD CARRIERS ; MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT,POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED,VISIBLE OR ULTRA-VIOLET LIGHT ; MEASURING ; PHYSICS ; PRESENTATION OF DATA ; RADIATION PYROMETRY ; RECOGNITION OF DATA ; RECORD CARRIERS ; TESTING</subject><creationdate>2019</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20191108&DB=EPODOC&CC=CN&NR=110427818A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20191108&DB=EPODOC&CC=CN&NR=110427818A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WANG CHUNXIANG</creatorcontrib><creatorcontrib>SUN LIN</creatorcontrib><creatorcontrib>JIA SHANGFENG</creatorcontrib><title>Deep learning satellite data cloud detection method supported by hyperspectral data</title><description>The invention discloses a deep learning satellite data cloud detection method supported by hyperspectral data. The method comprises the following steps: selecting enough cloud and clear sky pixels toconstruct a hyperspectral data sample library, and performing analog computation on the hyperspectral pixel sample library according to parameters such as a spectral response function and a waveband width of a to-be-detected sensor to obtain a cloud and clear sky surface pixel library of the to-be-detected sensor; based on a Keras deep learning framework, designing a deep BP neural network for cloud detection, inputting multispectral sample data obtained through simulation into the network for training and learning, and obtaining a multispectral sensor cloud detection rule based on spectral characteristics; based on a Markov random field model, optimizing a cloud detection result by utilizing an iterative condition mode algorithm, and removing a part of misclassification and misclassification errors of cloud detec</description><subject>CALCULATING</subject><subject>COLORIMETRY</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT,POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED,VISIBLE OR ULTRA-VIOLET LIGHT</subject><subject>MEASURING</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RADIATION PYROMETRY</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2019</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyzEKAjEQRuE0FqLeYTyAYFRwW1kVKxvtl3HzrxuIyZDMFnt7RTyA1Wu-NzW3IyAUwDn6-KTCihC8ghwrUxvS4MhB0apPkV7QPjkqg0jKCkePkfpRkIt8RObw3eZm0nEoWPw6M8vz6V5fVpDUoAi3iNCmvlq73m32la0O23_MG4cbOOE</recordid><startdate>20191108</startdate><enddate>20191108</enddate><creator>WANG CHUNXIANG</creator><creator>SUN LIN</creator><creator>JIA SHANGFENG</creator><scope>EVB</scope></search><sort><creationdate>20191108</creationdate><title>Deep learning satellite data cloud detection method supported by hyperspectral data</title><author>WANG CHUNXIANG ; SUN LIN ; JIA SHANGFENG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN110427818A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2019</creationdate><topic>CALCULATING</topic><topic>COLORIMETRY</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT,POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED,VISIBLE OR ULTRA-VIOLET LIGHT</topic><topic>MEASURING</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RADIATION PYROMETRY</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>WANG CHUNXIANG</creatorcontrib><creatorcontrib>SUN LIN</creatorcontrib><creatorcontrib>JIA SHANGFENG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WANG CHUNXIANG</au><au>SUN LIN</au><au>JIA SHANGFENG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Deep learning satellite data cloud detection method supported by hyperspectral data</title><date>2019-11-08</date><risdate>2019</risdate><abstract>The invention discloses a deep learning satellite data cloud detection method supported by hyperspectral data. The method comprises the following steps: selecting enough cloud and clear sky pixels toconstruct a hyperspectral data sample library, and performing analog computation on the hyperspectral pixel sample library according to parameters such as a spectral response function and a waveband width of a to-be-detected sensor to obtain a cloud and clear sky surface pixel library of the to-be-detected sensor; based on a Keras deep learning framework, designing a deep BP neural network for cloud detection, inputting multispectral sample data obtained through simulation into the network for training and learning, and obtaining a multispectral sensor cloud detection rule based on spectral characteristics; based on a Markov random field model, optimizing a cloud detection result by utilizing an iterative condition mode algorithm, and removing a part of misclassification and misclassification errors of cloud detec</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COLORIMETRY COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT,POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED,VISIBLE OR ULTRA-VIOLET LIGHT MEASURING PHYSICS PRESENTATION OF DATA RADIATION PYROMETRY RECOGNITION OF DATA RECORD CARRIERS TESTING |
title | Deep learning satellite data cloud detection method supported by hyperspectral data |
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