Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing - Image 1

£50.35

CRC Press Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

Name: Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
Brand: CRC Press
Price: 50.35 GBP
Availability: InStock

Data Mining

This is the most expensive it has ever been. Walk away.

£50 today · previous high £50 · all-time low £48

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 50 days • 50 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 50 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £50 (34 days, 68.0%)

Price range: £48 - £50

Price levels: 2 different prices over 50 days

Description

In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.

Product Specifications

Brand: CRC Press
Format: paperback
ASIN: 0367571978
Category: Books > Subjects > Computing & Internet > Databases > Data Storage & Management > Data Mining
Domain: Amazon UK
Release Date: 30 June 2020
Listed Since: 29 May 2020

Barcode

No barcode data available

CRC Press Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing

This is the most expensive it has ever been. Walk away.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Object and Pattern Recognition in Remote Sensing: Modelling and Monitoring Environmental and Anthropogenic Objects and Change Processes

Remote Sensing Image Fusion (Signal and Image Processing of Earth Observations)

Remote Sensing and Digital Image Processing with R - Textbook and Lab Manual

Remote Sensing and Digital Image Processing with R

Environmental Remote Sensing and Systems Analysis

Remote Sensing Handbook, Volume I: Sensors, Data Normalization, Harmonization, Cloud Computing, and Accuracies: 1

Remote Sensing for Sustainability (Remote Sensing Applications Series)

Analysis of Multi-temporal Remote Sensing Images: Proceedings of the First International Conference on Multitemp 2001, University of Trento, Italy, 13-14 September 2001 (Series in Remote Sensing): 2

Remote Sensing Applications for the Urban Environment

Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python

Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges (Computing and Networks)

Multitemporal Remote Sensing: Methods and Applications: 20 (Remote Sensing and Digital Image Processing, 20)

Remote Sensing Time Series Image Processing (Imaging Science)

CRC Press - Image Processing and Data Analysis with ERDAS IMAGINE

Remote Sensing Big Data (Springer Remote Sensing/Photogrammetry)

Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences

Computational Intelligence for Remote Sensing: 133 (Studies in Computational Intelligence, 133)

Urban High-Resolution Remote Sensing: Algorithms and Modeling

Essential Image Processing and GIS for Remote Sensing (Essential (John Wiley & Sons))

Springer Microwave Remote Sensing Tools in Environmental Science

Remote Sensed Data and Processing Methodologies for 3D Virtual Reconstruction and Visualization of Complex Architectures

Deep Learning for Remote Sensing Images with Open Source Software (Signal and Image Processing of Earth Observations)

Remote Sensing Time Series: Revealing Land Surface Dynamics: 22 (Remote Sensing and Digital Image Processing, 22)

Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data