Remote sensing image object level change detection method
The invention discloses a remote sensing image object level change detection method. The method specifically comprises the steps of image preliminary processing, mean filtering obtaining calculation and image analysis based on a convolutional neural network. The invention provides a self-adaptive k...
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| creator | LU YAN JIANG RUIBO SHA CONGSHU LIU SHAOTANG ZHANG HUIFENG XIE RUI ZHAN XIANYUN WU JUN YANG MINGDONG LIU XIAOQIANG XU LIANG YANG FUQIN CHEN CHAO WEN RUI XIAO HAIHONG XU CHENGGONG PAN JIECHEN ZHANG DI XU HAIWEI ZHANG SHUHUA WANG GUO CAI QINGKONG |
| description | The invention discloses a remote sensing image object level change detection method. The method specifically comprises the steps of image preliminary processing, mean filtering obtaining calculation and image analysis based on a convolutional neural network. The invention provides a self-adaptive k value calculation method, and provides a remote sensing image object level change detection method.The preliminary graph is acquired on the basis of the deep full convolutional neural network model as a whole, and the image is finally subjected to binarization processing on the basis of a corresponding algorithm, so that digital processing of the image is realized, later change detection can be conveniently carried out on the image on the basis of the digital processing of the image, and the detection precision and accuracy are improved.
本发明公开了一种遥感图像对象层次的变化检测方法，具体包括具体包括图像初步处理、均值滤波获取计算以及基于卷积神经网络的图像分析；本发明提出了一个自适应的k值计算方法本发明提出了一种遥感图像对象层次的变化检测方法，整体基于深度全卷积神经网络模型来获取初步图形，并基于对应的算法最终将图像进行二值化处理，从而实现图像的数字化处理，此时基于图像的数字化处理可以方便的对 |
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本发明公开了一种遥感图像对象层次的变化检测方法，具体包括具体包括图像初步处理、均值滤波获取计算以及基于卷积神经网络的图像分析；本发明提出了一个自适应的k值计算方法本发明提出了一种遥感图像对象层次的变化检测方法，整体基于深度全卷积神经网络模型来获取初步图形，并基于对应的算法最终将图像进行二值化处理，从而实现图像的数字化处理，此时基于图像的数字化处理可以方便的对</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</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=20190709&DB=EPODOC&CC=CN&NR=109993104A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25555,76308</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20190709&DB=EPODOC&CC=CN&NR=109993104A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LU YAN</creatorcontrib><creatorcontrib>JIANG RUIBO</creatorcontrib><creatorcontrib>SHA CONGSHU</creatorcontrib><creatorcontrib>LIU SHAOTANG</creatorcontrib><creatorcontrib>ZHANG HUIFENG</creatorcontrib><creatorcontrib>XIE RUI</creatorcontrib><creatorcontrib>ZHAN XIANYUN</creatorcontrib><creatorcontrib>WU JUN</creatorcontrib><creatorcontrib>YANG MINGDONG</creatorcontrib><creatorcontrib>LIU XIAOQIANG</creatorcontrib><creatorcontrib>XU LIANG</creatorcontrib><creatorcontrib>YANG FUQIN</creatorcontrib><creatorcontrib>CHEN CHAO</creatorcontrib><creatorcontrib>WEN RUI</creatorcontrib><creatorcontrib>XIAO HAIHONG</creatorcontrib><creatorcontrib>XU CHENGGONG</creatorcontrib><creatorcontrib>PAN JIECHEN</creatorcontrib><creatorcontrib>ZHANG DI</creatorcontrib><creatorcontrib>XU HAIWEI</creatorcontrib><creatorcontrib>ZHANG SHUHUA</creatorcontrib><creatorcontrib>WANG GUO</creatorcontrib><creatorcontrib>CAI QINGKONG</creatorcontrib><title>Remote sensing image object level change detection method</title><description>The invention discloses a remote sensing image object level change detection method. The method specifically comprises the steps of image preliminary processing, mean filtering obtaining calculation and image analysis based on a convolutional neural network. The invention provides a self-adaptive k value calculation method, and provides a remote sensing image object level change detection method.The preliminary graph is acquired on the basis of the deep full convolutional neural network model as a whole, and the image is finally subjected to binarization processing on the basis of a corresponding algorithm, so that digital processing of the image is realized, later change detection can be conveniently carried out on the image on the basis of the digital processing of the image, and the detection precision and accuracy are improved.
本发明公开了一种遥感图像对象层次的变化检测方法，具体包括具体包括图像初步处理、均值滤波获取计算以及基于卷积神经网络的图像分析；本发明提出了一个自适应的k值计算方法本发明提出了一种遥感图像对象层次的变化检测方法，整体基于深度全卷积神经网络模型来获取初步图形，并基于对应的算法最终将图像进行二值化处理，从而实现图像的数字化处理，此时基于图像的数字化处理可以方便的对</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2019</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLAMSs3NL0lVKE7NK87MS1fIzE1MT1XIT8pKTS5RyEktS81RSM5IzAOKpaSWAMUy8_MUclNLMvJTeBhY0xJzilN5oTQ3g6Kba4izh25qQX58anFBYnJqXmpJvLOfoYGlpaWxoYGJozExagA4uS6j</recordid><startdate>20190709</startdate><enddate>20190709</enddate><creator>LU YAN</creator><creator>JIANG RUIBO</creator><creator>SHA CONGSHU</creator><creator>LIU SHAOTANG</creator><creator>ZHANG HUIFENG</creator><creator>XIE RUI</creator><creator>ZHAN XIANYUN</creator><creator>WU JUN</creator><creator>YANG MINGDONG</creator><creator>LIU XIAOQIANG</creator><creator>XU LIANG</creator><creator>YANG FUQIN</creator><creator>CHEN CHAO</creator><creator>WEN RUI</creator><creator>XIAO HAIHONG</creator><creator>XU CHENGGONG</creator><creator>PAN JIECHEN</creator><creator>ZHANG DI</creator><creator>XU HAIWEI</creator><creator>ZHANG SHUHUA</creator><creator>WANG GUO</creator><creator>CAI QINGKONG</creator><scope>EVB</scope></search><sort><creationdate>20190709</creationdate><title>Remote sensing image object level change detection method</title><author>LU YAN ; JIANG RUIBO ; SHA CONGSHU ; LIU SHAOTANG ; ZHANG HUIFENG ; XIE RUI ; ZHAN XIANYUN ; WU JUN ; YANG MINGDONG ; LIU XIAOQIANG ; XU LIANG ; YANG FUQIN ; CHEN CHAO ; WEN RUI ; XIAO HAIHONG ; XU CHENGGONG ; PAN JIECHEN ; ZHANG DI ; XU HAIWEI ; ZHANG SHUHUA ; WANG GUO ; CAI QINGKONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN109993104A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2019</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>LU YAN</creatorcontrib><creatorcontrib>JIANG RUIBO</creatorcontrib><creatorcontrib>SHA CONGSHU</creatorcontrib><creatorcontrib>LIU SHAOTANG</creatorcontrib><creatorcontrib>ZHANG HUIFENG</creatorcontrib><creatorcontrib>XIE RUI</creatorcontrib><creatorcontrib>ZHAN XIANYUN</creatorcontrib><creatorcontrib>WU JUN</creatorcontrib><creatorcontrib>YANG MINGDONG</creatorcontrib><creatorcontrib>LIU XIAOQIANG</creatorcontrib><creatorcontrib>XU LIANG</creatorcontrib><creatorcontrib>YANG FUQIN</creatorcontrib><creatorcontrib>CHEN CHAO</creatorcontrib><creatorcontrib>WEN RUI</creatorcontrib><creatorcontrib>XIAO HAIHONG</creatorcontrib><creatorcontrib>XU CHENGGONG</creatorcontrib><creatorcontrib>PAN JIECHEN</creatorcontrib><creatorcontrib>ZHANG DI</creatorcontrib><creatorcontrib>XU HAIWEI</creatorcontrib><creatorcontrib>ZHANG SHUHUA</creatorcontrib><creatorcontrib>WANG GUO</creatorcontrib><creatorcontrib>CAI QINGKONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LU YAN</au><au>JIANG RUIBO</au><au>SHA CONGSHU</au><au>LIU SHAOTANG</au><au>ZHANG HUIFENG</au><au>XIE RUI</au><au>ZHAN XIANYUN</au><au>WU JUN</au><au>YANG MINGDONG</au><au>LIU XIAOQIANG</au><au>XU LIANG</au><au>YANG FUQIN</au><au>CHEN CHAO</au><au>WEN RUI</au><au>XIAO HAIHONG</au><au>XU CHENGGONG</au><au>PAN JIECHEN</au><au>ZHANG DI</au><au>XU HAIWEI</au><au>ZHANG SHUHUA</au><au>WANG GUO</au><au>CAI QINGKONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Remote sensing image object level change detection method</title><date>2019-07-09</date><risdate>2019</risdate><abstract>The invention discloses a remote sensing image object level change detection method. The method specifically comprises the steps of image preliminary processing, mean filtering obtaining calculation and image analysis based on a convolutional neural network. The invention provides a self-adaptive k value calculation method, and provides a remote sensing image object level change detection method.The preliminary graph is acquired on the basis of the deep full convolutional neural network model as a whole, and the image is finally subjected to binarization processing on the basis of a corresponding algorithm, so that digital processing of the image is realized, later change detection can be conveniently carried out on the image on the basis of the digital processing of the image, and the detection precision and accuracy are improved.
本发明公开了一种遥感图像对象层次的变化检测方法，具体包括具体包括图像初步处理、均值滤波获取计算以及基于卷积神经网络的图像分析；本发明提出了一个自适应的k值计算方法本发明提出了一种遥感图像对象层次的变化检测方法，整体基于深度全卷积神经网络模型来获取初步图形，并基于对应的算法最终将图像进行二值化处理，从而实现图像的数字化处理，此时基于图像的数字化处理可以方便的对</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Remote sensing image object level change detection method |
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