Abstract
Abstract. Unsupervised change detection of agricultural lands in seasonal and annual periods is necessary for farming activities and yield estimation. Polarimetric Synthetic Aperture Radar (PolSAR) data due to their special characteristics are a powerful source to study temporal behaviour of land cover types. PolSAR data allows building up the powerful observations sensitive to the shape, orientation and dielectric properties of scatterers and allows the development of physical models for identification and separation of scattering mechanisms occurring inside the same region of observed lands. In this paper an unsupervised kernel-based method is introduced for agricultural change detection by PolSAR data. This method works by transforming data into higher dimensional space by kernel functions and clustering them in this space. Kernel based c-means clustering algorithm is employed to separate the changes classes from the no-changes. This method is a non-linear algorithm which considers the contextual information of observations. Using the kernel functions helps to make the non-linear features more separable in a linear space. In addition, use of eigenvectors' parameters as a polarimetric target decomposition technique helps us to consider and benefit physical properties of targets in the PolSAR change detection. Using kernel based c-means clustering with proper initialization of the algorithm makes this approach lead to great results in change detection paradigm.
Highlights
Polarimetric Synthetic Aperture Radar (PolSAR) data provide high resolution, weather-independent, day-and-night images for various remote sensing applications such as land cover change detection
Several unsupervised change detection techniques including the very simple idea of image differencing and Change Vector Analysis (CVA) to more sophisticated statistical modeling of changes in images have been reviewed in literature (Radke et al 2005)
Bovolo et al (2008) proposed an algorithm based on the Support Vector Machines (SVM) as unsupervised change detection algorithm which uses the binary classification of change and no-change classes (Bovolo, Bruzzone, and Marconcini 2008)
Summary
Polarimetric Synthetic Aperture Radar (PolSAR) data provide high resolution, weather-independent, day-and-night images for various remote sensing applications such as land cover change detection. These types of data allows to build up the powerful observations sensitive to shape, orientation and dielectric properties of the scatterers and allows the development of physical models for identification and separation of scattering mechanisms occurring inside the same region of observed land cover. To benefit the polarimetric characteristics of PolSAR data, we used H-A-α decomposition (or Cloude-Pottier) which is derived from the eigenvectors and the eigenvalues of coherency matrix of original scattering matrix observation This decomposition has well adaption for agricultural applications and can help us to separate different scattering mechanisms of agricultural lands.
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