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 monit...
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.
