Application of Polsar For

نویسندگان

  • M. A. Raimadoya
  • B. H. Trisasongko
چکیده

This paper reports the result of the study in the AOE869 test site by exploring ASAR/APP dual polarization (VV, VH) image. The result is very promising, as it could differentiate clearly two land cover types : forest timber plantation and oil palm plantation. Although they play an important role for the monitoring of deforestation, these land covers were difficult to be differentiated in high resolution optical imageries (Landsat, SPOT,ASTER),. This ability is also required to monitor leakage in the project area for CDM A/R purpose of the Kyoto Protocol. ESA policy for the provision of free PolSAR PRO analysis software has given a concrete multiplier effect for the future potential market of polarimetric SAR data, especially in the developing countries.

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

Estimation of Normalized Coherency Matrix through the Sirv Model. Application to High Resolution Polsar Data

In the context of non-Gaussian polarimetric clutter models, this paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors (SIRV) modelling for coherency matrix estimation in heterogeneous clutter. The complete description of the POLSAR data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency...

متن کامل

Optimum Ensemble Classification for Fully Polarimetric SAR Data Using Global-Local Classification Approach

In this paper, a proposed ensemble classification for fully polarimetric synthetic aperture radar (PolSAR) data using a global-local classification approach is presented. In the first step, to perform the global classification, the training feature space is divided into a specified number of clusters. In the next step to carry out the local classification over each of these clusters, which cont...

متن کامل

A Non-Parametric Texture Descriptor for Polarimetric SAR Data with Applications to Supervised Classification

The paper describes a novel representation of polarimetric SAR (PolSAR) data that is inherently non-parametric and therefore particularly suited for characterising data in which the commonly adopted hypothesis of Gaussian backscatter is not appropriate. The descriptor is also non-local and can capture image structure in terms of the arrangement of edge, ridgeand point-like features, to yield a ...

متن کامل

Bias Compensation in H/A/α Polarimetric SAR Decomposition and Its Implication for the Classification

Classification of land cover types is one important application of polarimetric synthetic aperture radar (PolSAR) remote sensing. There are numerous features that can be extracted from PolSAR images. Among them, eigenvalues λi, entropy H, alpha angle α, and anisotropy A are effective and popular tools for the analysis and quantitative estimation of the physical parameters. Nevertheless, the spe...

متن کامل

Hierarchical Classification of Polarimetric Sar Image Based on Statistical Region Merging

Segmentation and classification of polarimetric SAR (PolSAR) imagery are very important for interpretation of PolSAR data. This paper presents a new object-oriented classification method which is based on Statistical Region Merging (SRM) segmentation algorithm and a two-level hierarchical clustering technique. The proposed method takes full advantage of the polarimetric information contained in...

متن کامل

PolSAR Image Classification Based on Deep Convolutional Neural Network

For introducing the advantages of feature learning and multilayer network in the interpretation of Polarimetric synthetic aperture radar (PolSAR) image, a classification algorithm based on deep convolutional neural network is proposed, and is used for PolSAR image classification. Firstly, a special convolutional neural network (CNN) for PolSAR image is constructed, secondly, a large number of P...

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 2007