Kernel Methods for Remote Sensing Data Analysis (Hardcover)

Gustavo Camps-Valls, Professor Lorenzo Bruzzone

  • 出版商: Wiley
  • 出版日期: 2009-12-01
  • 定價: $4,980
  • 售價: 9.0$4,482
  • 語言: 英文
  • 頁數: 434
  • 裝訂: Hardcover
  • ISBN: 0470722118
  • ISBN-13: 9780470722114
  • 相關分類: Data Science
  • 立即出貨 (庫存 < 3)

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商品描述

Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful  across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection.

 

Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges:

  • Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods.
  • Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection.
  • Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification.
  • Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. 
  • Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions.

This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

商品描述(中文翻譯)

核方法在機器學習和模式識別框架中長期以來一直被確立為有效的技術,現在已成為許多遙感應用的標準方法。通過結合統計和幾何算法,核方法已在從航空和衛星傳感器獲得的地球影像分析相關領域取得成功,包括自然資源控制、人工基礎設施(例如城市區域)的檢測和監測、農業盤點、災害預防和損害評估,以及異常和目標檢測。

本書介紹了與遙感領域相關的核方法(KMs)的理論基礎,並作為這些方法設計和實施的實用指南。五個不同的部分介紹了基於最新核方法進展的遙感相關研究,分析了相關的方法論和實際挑戰:

第一部分介紹了遙感機器學習的關鍵概念,以及核方法的理論和實踐基礎。

第二部分探討了監督式圖像分類,包括超向量機(SVMs)、核判別分析、多時序圖像分類、基於核的目標檢測,以及用於異常檢測的支持向量數據描述(SVDD)算法。

第三部分介紹了半監督分類,包括用於高光譜圖像分類和核均值數據分類的傳導式SVM方法。

第四部分討論了回歸和模型反演,包括用於高光譜影像的核解混算法概念,基於核方程的定量遙感反演問題的理論和方法,基於核的BRDF(雙向反射分布函數)以及溫度檢測KMs。

第五部分介紹了基於核的特徵提取,並對幾種多變量分析方法及其核擴展原則進行了回顧。

本書針對從事遙感數據處理的工程師、科學家和研究人員,以及從事機器學習和模式識別的人士。