{"product_id":"multi-sensor-and-multi-temporal-remote-sensing","title":"Multi-Sensor and Multi-Temporal Remote Sensing","description":"This 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.Key features:\u003cul\u003e \u003cul\u003e \u003c\/ul\u003e Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classesDiscusses range of fuzzy\/deep learning models capable to extract specific single class and separates noiseDescribes pre-processing while using spectral, textural, CBSI indices, and back scatter coefficient\/Radar Vegetation Index (RVI) Discusses the role of training data to handle the heterogeneity within a classSupports multi-sensor and multi-temporal data processing through in-house SMIC softwareIncludes case studies and practical applications for single class mapping\u003cul\u003e \u003c\/ul\u003e \u003c\/ul\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.","brand":"Taylor \u0026 Francis","offers":[{"title":"Default Title","offer_id":44556778504430,"sku":"9781032428321","price":131.2,"currency_code":"AUD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0630\/9612\/7726\/files\/9781032428321_1bfc38ed-b872-4ea3-98ad-61519337b85f.jpg?v=1704952146","url":"https:\/\/bookland.com.au\/products\/multi-sensor-and-multi-temporal-remote-sensing","provider":"Book Land AU","version":"1.0","type":"link"}