LEADER 04240nam 22007095 450 001 9910588591603321 005 20251113200340.0 010 $a981-19-3739-7 024 7 $a10.1007/978-981-19-3739-2 035 $a(MiAaPQ)EBC7076036 035 $a(Au-PeEL)EBL7076036 035 $a(CKB)24723841600041 035 $a(PPN)264197143 035 $a(OCoLC)1342593577 035 $a(DE-He213)978-981-19-3739-2 035 $a(EXLCZ)9924723841600041 100 $a20220818d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRemote Sensing Intelligent Interpretation for Mine Geological Environment $eFrom Land Use and Land Cover Perspective /$fby Weitao Chen, Xianju Li, Lizhe Wang 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (254 pages) 225 1 $aEarth and Environmental Science Series 311 08$aPrint version: Chen, Weitao Remote Sensing Intelligent Interpretation for Mine Geological Environment Singapore : Springer,c2022 9789811937385 327 $aPreface.-Mine geological environment: An overview.-Multimodal remote sensing science and technology.-Deep learning technology for remote sensing intelligent interpretation.-Remote sensing interpretation signs of mine land occupation type -- Mine remote sensing dataset construction for multi-level tasks -- Mine target detection by remote sensing and deep learning -- Mine remote sensing scene classification by deep learning -- Mine land occupation classification based on machine learning and remote sensing images -- Mine land occupation classification based on deep learning and remote sensing images -- Concluding remarks. 330 $aThis book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of ?target detection?scene classification?semantic segmentation." Taking China?s Hubei Province as an example, this book focuses on the following four aspects: 1. Development of a multiscale remote sensing dataset of the mining area, including mine target remote sensing dataset, mine (including non-mine areas) remote sensing scene dataset, and semantic segmentation remote sensing dataset of mining land cover. The three datasets are the basis of intelligent interpretation based on deep learning. 2. Research on mine target remote sensing detection method based on deep learning. 3. Research on remote sensing scene classification method of mine and non-mine areas based on deep learning. 4. Research on the fine-scale classification method of mining land cover based on semantic segmentation. The book is a valuable reference both for scholars, practitioners and as well as graduate students who are interested in mining environment research. 410 0$aEarth and Environmental Science Series 606 $aGeographic information systems 606 $aMachine learning 606 $aSignal processing 606 $aGeology 606 $aEnvironmental monitoring 606 $aGeographical Information System 606 $aMachine Learning 606 $aSignal, Speech and Image Processing 606 $aGeology 606 $aEnvironmental Monitoring 615 0$aGeographic information systems. 615 0$aMachine learning. 615 0$aSignal processing. 615 0$aGeology. 615 0$aEnvironmental monitoring. 615 14$aGeographical Information System. 615 24$aMachine Learning. 615 24$aSignal, Speech and Image Processing. 615 24$aGeology. 615 24$aEnvironmental Monitoring. 676 $a006.31 700 $aChen$b Weitao$01254062 702 $aLi$b Xianju 702 $aWang$b Lizhe$f1974- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910588591603321 996 $aRemote sensing intelligent interpretation for mine geological environment$93363944 997 $aUNINA