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: Focuses on use of multi-sensor, multi-temporal data while handling spectral overlap between classes Discusses range of fuzzy/deep learning models capable to extract specific single class and separates noise Describes 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 class Supports multi-sensor and multi-temporal data processing through in-house SMIC software Includes case studies and practical applications for single class mapping This 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.
Anil Kumar & Uttara Singh
Multi-Sensor and Multi-Temporal Remote Sensing [EPUB ebook]
Specific Single Class Mapping
Multi-Sensor and Multi-Temporal Remote Sensing [EPUB ebook]
Specific Single Class Mapping
Придбайте цю електронну книгу та отримайте ще 1 БЕЗКОШТОВНО!
Мова Англійська ● Формат EPUB ● Сторінки 178 ● ISBN 9781000872200 ● Видавець CRC Press ● Опубліковано 2023 ● Завантажувані 3 разів ● Валюта EUR ● Посвідчення особи 8906447 ● Захист від копіювання Adobe DRM
Потрібен читач електронних книг, що підтримує DRM