{"product_id":"9781032428321-multisensor-multitemporal-remote-sensi","title":"Multi-Sensor and Multi-Temporal Remote Sensing","description":"\u003cmeta content=\"text\/html; charset=utf-8\" http-equiv=\"Content-Type\"\u003e\u003cp\u003e\u003cspan\u003eSpecific Single Class Mapping. This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, fuzzy machine learning models supported by case studies. \u003cp\u003eThis book elaborates fuzzy machine and deep learning models for single class mapping from multi-sensor, multi-temporal remote sensing images while handling mixed pixels and noise. It also covers the ways of pre-processing and spectral dimensionality reduction of temporal data. Further, it discusses the ‘individual sample as mean’ training approach to handle heterogeneity within a class. The appendix section of the book includes case studies such as mapping crop type, forest species, and stubble burnt paddy fields.\u003c\/p\u003e\n\u003cp\u003eKey features:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eFocuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes\u003c\/li\u003e\n\u003cli\u003eDiscusses range of fuzzy\/deep learning models capable to extract specific single class and separates noise\u003c\/li\u003e\n\u003cli\u003eDescribes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient\/Radar Vegetation Index (RVI) \u003c\/li\u003e\n\u003cli\u003eDiscusses the role of training data to handle the heterogeneity within a class\u003c\/li\u003e\n\u003cli\u003eSupports multi-sensor and multi-temporal data processing through in-house SMIC software\u003c\/li\u003e\n\u003cli\u003eIncludes case studies and practical applications for single class mapping\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eThis book is intended for graduate\/postgraduate students, research scholars, and professionals working in environmental, geography, computer sciences, remote sensing, geoinformatics, forestry, agriculture, post-disaster, urban transition studies, and other related areas.\u003c\/p\u003e\n\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Rarewaves","offers":[{"title":"Default Title","offer_id":40823512531041,"sku":"9781032428321","price":101.39,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0092\/7504\/8033\/products\/stand_14079331_jpg.jpg?v=1705546619","url":"https:\/\/www.rarewaves.com\/products\/9781032428321-multisensor-multitemporal-remote-sensi","provider":"Rarewaves.com","version":"1.0","type":"link"}