Abstract
Changes in the earth's surface significantly increase natural disasters, resulting in severe damage to man-made objects, such as roads, buildings, bridges, and so on. Radar techniques have advantages, such as lack of sensitivity to weather conditions, to night and day, and to cloud cover conditions, which can be used to identify, alert, and mitigate these damages. Because of the importance of these areas and the need to care for them, land-use classification, one of the important applications of remote sensing, is performed. Polarimetric synthetic aperture radar (PolSAR) images have many capabilities, having the scattering information on four polarized levels (HH, HV, VH and VV) and consequently depending on the shape and structure of the environment. In this study, unmaned aerial vehicle (UAVSAR) image is used. The support vector machine (SVM) model is a well-known classification method, able to run on different types of features and to distinguish classes that are not linearly separable. On the other hand, it is possible to use data mining methods to facilitate data analysis, like classifications. In this regards, it is recommended to use the random forest (RF) technique. The RF is one of the useful methods for data classification which uses a tree structure for decision-making. This method uses strategies to enhance the probability of reaching goals with conditional probability. In this study, by incorporating a variety of target decomposition methods in PolSAR images, images producing the land cover types were generated. Then, 70 features were obtained by applying the support vector machine (SVM), random forest (RF) , and K-nearest neighbor (KNN) classification methods. In order to estimate accuracy, the output of these methods was evaluated by reference data.
Highlights
Changes in the earth’s surface significantly increase natural disasters, resulting in severe damage to man-made objects, such as roads, buildings, bridges, and so on
The main basis of this method is a linear classification of data, by taking safety margins into account, and is basically considered as a binary separator with the main goal of reaching the optimized hyper-plane to increase the boundary of two classes
We examined the classification methods, including support vector machine (SVM), Knearest neighbor (KNN), and random forest (RF), in order to compare them with respect to classification speed with appropriate accuracy
Summary
The use of remote sensing data as an ideal source of precision and speed of operation has become one of the most important means of data collection. An overview of the research has been briefly described in a variety of classification methods These studies have succeeded in classifying radar images, they focus on only one classification algorithm. The most important of these are the K-nearest neighboring (KNN) methods and the SVM and RF algorithms These three classification methods are recognized as the most suitable models for optimizing the process of classification of remote sensing images [5]. Different types of distribution matrix elements are used for the production of the features These algorithms used a special manner for producing aclassification map-based training data. The third category is obtained on the basis of the eigenvector and eigenvalues of the covariance matrix (or coherence matrix) Some of these methods are used according to their application, such as Holm, Van, Cloud, Zyl and Could, Pottier, and Could. The presented types of algorithms such as Cloud Pottier, Freeman, Krogager, Van Zyl; presented to produce the features
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