Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
暫譯: 模糊機器學習演算法在遙感影像分類中的應用

Kumar, Anil, Upadhyay, Priyadarshi, Kumar, A. Senthil

Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
  • 出版商: CRC
  • 出版日期: 2023-09-25
  • 售價: $2,350
  • 貴賓價: 9.5 折 $2,233
  • 語言: 英文
  • 頁數: 194
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0367355744
  • ISBN-13: 9780367355746
  • 相關分類: Machine Learning、Algorithms-data-structures

    • 立即出貨 (庫存=1)

商品描述

This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels.

Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to:

  • exclusive focus on using large range of fuzzy classification algorithms for remote sensing images;

  • discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images;

  • describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms;

  • explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and;

  • combines explanation of the algorithms with case studies and practical applications.

商品描述(中文翻譯)

這本書涵蓋了最先進的影像分類方法，旨在從遙感衛星數據中區分地球物體，特別強調模糊機器學習和深度學習演算法。這兩類演算法的描述詳細到可以直接用於從多光譜光學遙感數據中進行多類別或特定類別的主題映射。這些演算法結合多日期、多感測器的遙感技術，能夠監測特定階段（例如，作物生長的物候）的一個特定類別。憑藉這些能力，模糊機器學習演算法在作物保險、森林火災映射、秸稈焚燒、災後損害映射等領域具有強大的應用潛力。本書還提供了使用所提出的基於類別的感測器獨立（CBSI）方法的時間指數數據庫的詳細信息，並附有實際範例。此外，本書還探討了基於距離、基於核以及通過馬可夫隨機場（MRF）/局部卷積方法處理混合像素、非線性和噪聲像素的其他相關演算法。

進一步地，本書涵蓋了從軟分類中獲得的軟分類分數輸出進行定量評估的技術，並由內部開發的工具稱為子像素多光譜影像分類器（SMIC）支持。該書旨在針對地理資訊科學、地理學、電氣、電子和計算機科學等不同領域的研究生、碩士生、研究學者和在地球觀測及衛星影像處理領域工作的專業人士。書中討論的學習演算法在其他相關領域（例如醫學影像）中也可能有用。總體而言，本書的目標是：

- 專注於使用各種模糊分類演算法處理遙感影像；
- 討論人工神經網絡（ANN）、卷積神經網絡（CNN）、遞歸神經網絡（RNN）及混合學習分類器在遙感影像上的應用；
- 描述子像素多光譜影像分類器工具（SMIC），以支持所討論的模糊和學習演算法；
- 解釋如何使用模糊誤差矩陣（FERM）及其進階版本，通過FERM工具、熵、相關係數、均方根誤差和接收者操作特徵（ROC）方法來評估軟分類輸出作為分數影像；
- 結合演算法的解釋與案例研究和實際應用。

作者簡介

Anil Kumar is working as Scientist/Engineer-'SG' & Head Photogrammetry and Remote Sensing Department at Indian Institute of Remote Sensing (IIRS), Indian Space Research organisation (ISRO), Dehradun, India. He received his B.Tech degree in Civil Engineering from University of Lucknow, India and M.E. degre as well as inservise Ph.D in soft computing from Indian Institute of Technology, Roorkee, India. He has published 46 papers in journals. Guided 36 masters and 5 Ph.D thesis. He has been recipient of the prestigious P. R. Pisharoty Memorial Award conferred by the Indian Society of Remote Sensing. He is a life member of the Indian Society of Remote Sensing. His current research interests are in Soft computing, Deep Learning, Multi-sensor temporal data processing, Digital Photogrammetry, GPS and LiDAR.

Priyadarshi Upadhyay is working as a Scientist/Engineer-SD in Uttarakhand Space Application Centre (USAC), Dehradun, India. He received his M.Sc. degree in Physics from Kumaun University Nainital, India and M.Tech. degree in Remote Sensing from Birla Institute of Technology, Mesra Ranchi, India. He has received his Ph.D. degree from Indian Institute of Technology Roorkee, India in the area of time series remote sensing for single crop identification. He has published 15 research papers in various International Journals, Internation and National Conferences. He has been awarded by presitigious CSIR-NET, GATE and MHRD Travel Grant Fellowships. He is a life member of Indian Society of Remote Sensing and The Institute of Engineers (India). His current research interest are Microwave Remote Sensing for Soil Moisture and Crop Mapping, Polarimatric and Inerferrometric SAR, Hyperspectral and Optical Remote Sensing, Climate Change, Ecological Studies in Himalayan Region for Economically Important Crops and Plants.

A. Senthil Kumar is the Director of UN-affliated Centre for Space Science and Technology Education in Asia and the Pacific in Dehradun, India. He received M.Sc. (Engg.) and Ph.D. from the Indian Institute of Science, Bangalore in the field of image processing in 1985 and 1990 respectively. He joined ISRO in 1991. Since then he has served in Indian Remote Sensing programs in various capacities. He has published more than 120 technical papers in international journals and conferences and co-edited a book on Remote Sensing of Northwest Himalayan Ecosystems. He has received ISRO Team awards for his contributions to Chandrayaan-1 and Cartosat-1 missions. His research areas include remote sensing sensor characterization, radiometric data processing, image restoration, data fusion techniques and in soft computing techniques. He has also been a recipient of the prestigious Prof. Satish Dhawan Award conferred by the Indian Society of Remote Sensing. He is a life member of the Indian Society of Remote Sensing and the Indian Society of Geomatics.

作者簡介(中文翻譯)

Anil Kumar 目前擔任印度空間研究組織（ISRO）德拉敦的印度遙感研究所（IIRS）科學家/工程師-‘SG’及攝影測量與遙感部門負責人。他在印度勒克瑙大學獲得土木工程的學士學位，並在印度理工學院魯爾基獲得軟計算的碩士學位及在職博士學位。他已在期刊上發表了46篇論文，指導了36篇碩士論文和5篇博士論文。他曾獲得由印度遙感學會頒發的著名P. R. Pisharoty紀念獎。他是印度遙感學會的終身會員。他目前的研究興趣包括軟計算、深度學習、多感測器時間數據處理、數位攝影測量、GPS和LiDAR。

Priyadarshi Upadhyay 目前在印度德拉敦的烏塔拉坎德空間應用中心（USAC）擔任科學家/工程師-SD。他在印度奈尼塔爾的庫瑪雲大學獲得物理學碩士學位，並在印度美斯拉的比爾拉科技學院獲得遙感的碩士學位。他在印度理工學院魯爾基獲得博士學位，研究主題為單一作物識別的時間序列遙感。他在各種國際期刊和國際及國內會議上發表了15篇研究論文，並獲得了著名的CSIR-NET、GATE和MHRD旅行獎學金。他是印度遙感學會和印度工程師學會的終身會員。他目前的研究興趣包括土壤濕度和作物繪圖的微波遙感、極化和干涉合成孔徑雷達、超光譜和光學遙感、氣候變遷、以及對喜馬拉雅地區經濟重要作物和植物的生態研究。

A. Senthil Kumar 是聯合國附屬的亞洲及太平洋空間科學與技術教育中心的主任，位於印度德拉敦。他於1985年和1990年在班加羅爾的印度科學研究院獲得碩士（工程）和博士學位，專攻影像處理。他於1991年加入ISRO，並自此在印度遙感計畫中擔任多個職位。他在國際期刊和會議上發表了超過120篇技術論文，並共同編輯了一本有關西北喜馬拉雅生態系統遙感的書籍。他因對Chandrayaan-1和Cartosat-1任務的貢獻而獲得ISRO團隊獎。他的研究領域包括遙感傳感器特性、輻射數據處理、影像修復、數據融合技術及軟計算技術。他也曾獲得由印度遙感學會頒發的著名Prof. Satish Dhawan獎。他是印度遙感學會和印度地理信息學會的終身會員。

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