International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Image Classification For SAR Images Using Modified ANN Prateek Priyadarshini1, Col. Dr. O.P Malik 2 P.G Student, Dept. of Electronics & communication, Al-Falah University, Haryana, India 2 Professor, Dept. of Electical & Electronics, Al-Falah University, Haryana, India ---------------------------------------------------------------------***--------------------------------------------------------------------1
Abstract - Classification of polarimetric SAR images has
become a very important topic after the availability of Polarimetric SAR images through different sensors like SIR-C, ALOS-PALSAR etc. An analyst attempts to classify features in an SAR image by using the elements of visual interpretation to identify homogeneous groups of pixels that represents various features or land cover classes of interest. There is need to devise accurate methods for classification of SAR images. The combinations of different polarizations from L- and C- band helps to improve the classification accuracy. It was found that the combinations of channels gave the best overall accuracies.. The proposed classifier is examined in Matlab with the help of Modified Artificial Neural Network using feed forward back propagation technique. The method finds 9 different land cover and sites. Key Words: Synthetic Aperture Radar(SAR), Artificial Neural Networks(ANN), Real Aperture Radar (RAR), Classification, Polarimetry
1. INTRODUCTION Synthetic Aperture Radar is a radar technology that is used from satellite or airplane. It produces high resolution images of earth‘s surface by using special signal processing techniques. Synthetic aperture radar has important role in gathering information about earth‘s surface because it can operate under all kinds of weather condition (whether it is cloudy, hazy or dark). Polarimetric SAR (PolSAR) image classification is arguably one of the most important applications in remote sensing. Classification is the process of assigning a set of given data elements to a given set of labels or classes such that various parameter of assigning the data element to a class is optimized. Radar polarimetry is a technique for classification of land use features. Various research work have reported the use of polarimetric data to map earth terrain types and land covers ([1], [2], [3], [4], [5]). Image classification can be mainly divided into supervised and unsupervised classification techniques. An unsupervised classification technique, classifies the image automatically by finding the clusters based on certain criterion. On the other hand in supervised classification technique the location and the identity of some cover type and terrain type , for example urban, forest, and water are known prior to us.The data is collected by a field work, maps, and personal experience. The analyst tries to locate these areas on the remotely sensed data. These areas are known as © 2017, IRJET
|
Impact Factor value: 5.181
|
“training sites”. An analyst can guide a classifier with the help of these training sites to learn the relationship between the data and the classes. This manual technique of selecting training sets could be difficult when ground truth is not available. In this paper a new technique is proposed using modified ANN. It is a supervised classification technique. The proposed method is tested and analyzed in MATLAB.
1.1 Literature Survey Both visual interpretation and automatic analysis of data from imaging radars are complicated by a fading effect called speckle, which manifests itself as a strong granularity in detected images (amplitude or intensity). For example, simple classification methods based on thresholding of gray levels are generally inefficient when applied to speckled images, due to the high degree of overlap between the distributions of the different classes. Speckle is caused by the constructive and destructive interference between waves returned from elementary scatterers within each resolution cell. It is generally modelled as a multiplicative random noise . Compared with optical image, SAR image has more legible outline, better contrast and more plentiful texture information. The objects of different shape and physical feature take on different texture character, which is a critical technique of identifying objects by radar. At present, there are many approaches to image classification, but there is not an approach to suit all kinds of images. During the past years, different methods were employed for classification of synthetic aperture radar (SAR) data, based on the Maximum Likelihood (ML), artificial Neural Networks (ANN) fuzzy methods or other approaches . The NN classifier depends only on the training data and the discrimination power of the features. Fukuda and Hirosawa applied wavelet-based texture feature sets for classification of multi frequency polarimetric SAR images. The Classification accuracy depends on quality of features and the employed classification algorithm. For a high resolution SAR image classification, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian Scattering statistics (Weibull, Log normal, Nakagami Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. For SAR image classification problem many fuzzy models have been proposed, Fuzzy c-means clustering (FCM) algorithm is widely applied in various areas such as image ISO 9001:2008 Certified Journal
|
Page 2924