Linear Spectral Mixture Models and Support VectorMachines for Remote

نویسندگان

  • Martin Brown
  • Hugh Lewis
  • Steve Gunn
چکیده

| Mixture modelling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve sub-pixel, area information. This paper compares a well-established technique, Linear Spectral Mixture Models (LSMM), with a much newer idea based on data selection, Support Vector Machines (SVM). It is shown that the constrained least squares LSMM is equivalent to the linear SVM, which relies on proving that the LSMM algorithm possesses the \maximum margin" property. This in turn shows that the LSMM algorithm can be derived from the same optimality conditions as the linear SVM, which provides important insights about the role of the bias term and rank deeciency in the pure pixel matrix within the LSMM algorithm. It also highlights one of the main advantages for using the linear SVM algorithm in that it performs automatic \pure pixel" selection from a much larger database. In addition, extensions to the basic SVM algorithm allow the technique to be applied to data sets which exhibit spectral confusion (overlapping sets of pure pixels) and to data sets which have non-linear mixture regions. Several illustrative examples, based on an area-labelled Landsat TM dataset, are used to demonstrate the potential of this approach.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data

The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum i...

متن کامل

Linear spectral mixture models and support vector machines for remote sensing

Mixture modeling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve subpixel, area information. This paper compares a well-established technique, linear spectral mixture models (LSMM), with a much newer idea based on data selection, support vector machines (SVM). It is shown that the constrained least squares LSMM is equivalent to the li...

متن کامل

A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks

A radial basis function neural network (RBFNN) is developed to examine two mixing models, linear and nonlinear spectral mixtures, which describe the spectra collected by both airborne and laboratory-based spectrometers. We examine the possibility that there may be naturally occurring situations where the typically used linear model may not provide the most accurate resultant spectral descriptio...

متن کامل

Identification of a Surface Marine Vessel Using LS-SVM

The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of the experimental systems is of key importance. In marine systems, the changing environmental conditions and the complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate simulations. There exist a wide number of techniques to definem...

متن کامل

Linear spectral random mixture analysis for hyperspectral imagery

Independent component analysis (ICA) has shown success in blind source separation and channel equalization. Its applications to remotely sensed images have been investigated in recent years. Linear spectral mixture analysis (LSMA) has been widely used for subpixel detection and mixed pixel classification. It models an image pixel as a linear mixture of materials present in an image where the ma...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000