POLARIMETRIC-SAR CLASSIFICATION USING FUZZY MAXIMUM LIKEHOOD ESTIMATION CLUSTERING WITH CONSIDERATION OF COMPLEMENTARY INFORMATION BASED ON PHYSICAL POLARIMETRIC PARAMETERS, TARGET SCATTERING CHARACTERISTIK, AND SPATIAL CONTEXT
DOI:
https://doi.org/10.30536/j.ijreses.2008.v5.a1225Keywords:
Cloudes polarimetric decomposition, FMLE clustering, polarimetric coherence, Polarimetric-SAR, unsupervised classificationAbstract
This paper shows a study on an alternative method for unsupervised classification of polarimetric-Syenthetic Aperture Radar (SAR) data. The first step was to extract several main physical polarimetric parameters (polarization power, coherence, and phase difference) from polarimetric covariance matrix (or coherency matrix) and physical scattering characteristics of land use/cover based on polarimetric decomposition (Cloude decomposition model). In this paper, we found that these features have complementary information which can be integrated in order to improve the discrimination of different land use or cover types. Classification stage was performed using Fuzzy Maximum Likelihood Estimation (FMLE) clustering algorithm. FMLE algorithm allows for ellipsoidal clusters of arbitrary extent and is consequently more flexible than standard Fuzzy K-Means clustering algorithm. Hoever, basic FMLE algorithm makes use exclusively the spectral (or intensity) properties of the individual pixel vectors and spatial-contextual information of the image was not taken into account. Hence, poor(noisy) classification result is ussualy obtained from SAR data due to speckle noise. In this paper, we propose a modified FMLE which integrate basic FMLE clustering with spatial-contextual information by statistical analysis of local neightbourhoods. The effectiveness of the proposed method was demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Result showed classified images improving land-cover discrimination performance. Exhibiting homogeneous region, and preserving edge and other fine structures.
Downloads
Published
Issue
Section
License
Copyright (c) 2008 Author (S)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Notice for the International Journal of Remote Sensing and Earth Sciences (IJReSES)
Copyright Holder: Author(s).
By submitting an article to IJReSES, author(s) agree to the following terms:
1. Grant of Publishing Rights: Authors grant IJReSES the license to publish the article and to identify itself as the original publisher. This includes the right to make the article available in all forms and media.
2. Commercial Rights: Authors grant IJReSES the rights to produce and sell hardcopy volumes of the journal. These volumes may be purchased by libraries, individuals, or other entities.
3. Third-Party Use: Authors agree to allow any third party to freely use the article, provided that the original authors are credited and the article is cited appropriately. This facilitates the dissemination and impact of the work.
4. Creative Commons License: The article is distributed under the Creative Commons Attribution non Commercial Share Alike 4.0 License (CC BY-NC-SA 4.0). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as the original author is credited for the original creation.
5. Associated Published Material: Unless otherwise stated, any associated published material (such as supplementary data, graphics, and multimedia) is distributed under the same CC BY-NC-SA 4.0 License.
By adhering to these terms, authors ensure the wide dissemination and accessibility of their work, contributing to the advancement of knowledge in the fields of remote sensing and earth sciences.
