Remote Sensing Image Classification in R
暫譯: R中的遙感影像分類

Name: Remote Sensing Image Classification in R
Price: 5539 TWD
Availability: OnlineOnly
Author: Kamusoko, Courage
ISBN: 9811380112

Kamusoko, Courage

出版商: Springer
出版日期: 2019-08-15
售價: $5,830
貴賓價: 9.5 折 $5,539
語言: 英文
頁數: 168
裝訂: Quality Paper - also called trade paper
ISBN: 9811380112
ISBN-13: 9789811380112

海外代購書籍(需單獨結帳)

商品描述

This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification.

This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification.

R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.

商品描述(中文翻譯)

本書介紹了如何使用機器學習演算法在 R 中進行遙感影像處理和分類。它還提供了一個簡明且實用的參考教程，使讀者能夠立即開始使用該軟體平台和 R 套件進行影像處理和分類。

本書分為五個章節。第一章介紹了 R 中的遙感數位影像處理，第二章涵蓋了前處理。第三章專注於影像轉換，第四章則探討影像分類。最後，第五章處理改善影像分類的問題。

R 的優勢在於它是開源軟體，免費提供，並包含一些商業軟體包中沒有的實用功能。本書對所有本科生和研究生、研究人員、大學教師以及其他對在 R 中實際實施遙感感興趣的從業者都有幫助。

作者簡介

Courage Kamusoko is a senior researcher at the Asia Air Survey, Japan. His expertise includes land use/cover change modeling, and the design and implementation of geospatial database management systems. His primary research interests are the analysis of remotely sensed images, land use/cover modeling, and machine learning. In addition to his focus on geospatial research and consultancy, he has also taught practical machine learning for geospatial analysis and modeling.