Pooja Mishra

LAND COVER CLASSIFICATION OF PALSAR IMAGES BY KNOWLEDGE BASED DECISION TREE CLASSIFIER AND SUPERVISED CLASSIFIERS BASED ON SAR OBSERVABLES

The intent of this paper is to explore the application of information obtained from fully polarimetric data for land cover classiflcation. Various land cover classiflcation techniques are available in the literature, but still uncertainty exists in labeling various clusters to their own classes without using any a priori information. Therefore, the present work is focused on analyzing useful intrinsic information extracted from SAR observables obtained by various decomposition techniques. The eigenvalue decomposition and Pauli decomposition have been carried out to separate classes on the basis of their scattering mechanisms. The various classiflcation techniques (supervised: minimum distance, maximum likelihood, parallelepiped and unsupervised: Wishart) were applied in order to see possible difierences among SAR observables in terms of information that they contain and their usefulness in classifying particular land cover type. Another important issue is labeling the clusters, and this work is carried out by decision tree classiflcation that uses knowledge based approach. This classifler is implemented by scrupulous knowledge of data obtained by empirical evidence and their experimental validation. It has been demonstrated quantitatively that standard polarimetric parameters such as polarized backscatter coe-cients (linear, circular

A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables

The polarimetric information contained in polarimetric synthetic aperture radar (SAR) images represents great potential for characterization of natural and urban surfaces. However, it is still challenging to identify different land cover classes with polarimetric data. Most of the classification algorithms presented earlier have used a fixed value of polarimetric indexes for segregation of a particular land cover type from other classes. However, the value of these polarimetric indexes may change accordingly with change in observation site, temporal acquisition, environmental conditions, and calibration differences among various systems. Thus, the value of polarimetric indexes for segregation of each land cover type has to be tuned in order to cope with these changes. Therefore, in this paper, a decision-tree-based adaptive land cover classification technique has been proposed for labeling of different clusters to their own classes. The proposed method uses spatial-statistics-based expressions (i.e., median “ M” and standard deviation “ S”) of best-selected polarimetric indexes on the basis of a separability index criterion for creating the decision boundary among various classes. In order to make the system adaptive in nature, unknown terms have been included in the expressions. Due to the dependence of a developed nonlinear relationship of overall classification accuracy (OA) on large number of unknowns, a genetic algorithm (GA) approach has been used, which provides optimum values of considered polarimetric indexes for automatic segregation of different classes. The proposed algorithm is successfully tested and validated on ALOS PALSAR quad-pol data.

Fusion of polarimetric channel information of PALSAR data for land cover classification

Fully polarimetric radars are capable of preserving detailed information about the targets because of the amplitude and phase information they contain which helps in distinguishing different scattering mechanisms. Therefore, nowadays it is needed to use this information for various applications, i.e., classification, target identification etc. In polarimetry, circular (L: left-hand circular polarization and R: right-hand circular polarization) as well as linear (H: horizontal polarization and V: vertical polarization) polarizations have their own advantages. In this paper, an attempt has been made to highlight the effect and importance of both types of polarizations for land-cover classification using ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array-type L-band Synthetic Aperture Radar) quad-polarimetric data. Polarization responses from certain targets in linear (i.e., HH, HV, VV) and circular (i.e., LL, LR, RR) bases have been extensively analysed. Supervised classification using minimum distance classifier is used to examine the performance of both the polarization bases for terrain classification. It has been observed that circular basis polarization may be suitable for better estimation of water class unlike linear basis polarization and inclusion of circular basis enhances the classification results. This type of study will be quite helpful in the near future to enhance the special features of terrain covers.

Probability density functions based study for identification of land cover using SAR data

Fully polarimetric SAR data has the ability of characterizing and differentiating various land covers as it conserves detailed information of the amplitude and the phase of backscattering coefficient, which helps in distinguishing diverse scattering mechanisms. The classification by means of polarimetric data could be enhanced by fusing it with statistical information, but labeling of different classes is still a challenge. So, in this paper, probability density function based approach has been proposed for identification of different classes of land cover. Land cover is classified into four classes using polarimetric indices information and then six probability density functions are applied on each of the classes. Chi-Squared goodness of fit (GoF) test has been used for selecting best-fit density function for each of the classes. The boundaries of the classes were estimated using scale and location parameter of the best-fit density function. The proposed approach was applied on ALOS PALSAR data which resulted in good identification of urban and water region.

An Approach for Finding Possible Presence of Water Ice Deposits on Lunar Craters Using MiniSAR Data

Investigating the feasibility of water-ice deposits on lunar surface has been a very challenging task, which requires meticulous effort. Conceptualization of MiniSAR was a breakthrough because it had capability to image the shadowed regions that may have higher possibility of water-ice. Earlier studies have found that circular polarization ratio (CPR) is greater than unity in regions having volume scattering due to dielectric mixing (or water-ice deposits). However, later experiments revealed that CPR > 1 might also occur due to surface roughness. Thus, instead of using single polarimetric parameter CPR, it is required to use textural (or roughness) behavior of lunar surface along with scattering mechanisms, for obtaining the regions having higher possibility of dielectric mixing. For this purpose, information of two different approaches namely, polarimetric approach (i.e., m - δ decomposition and m - χ decomposition) and fractal approach have been fused together. The polarimetric approaches, i.e., m - δ decomposition and m - χ decomposition, help in identifying scattering mechanisms associated with lunar surface, whereas fractal-based approach helps in characterizing lunar surface on the basis of surface roughness using a measure called fractal dimension “D.” Finally, a decision tree algorithm has been proposed, in which decision criteria are decided on the basis of CPR, m - δ decomposition, m - χ decomposition, and fractal dimension “D.” The proposed approach seems to resolve the vagueness caused by CPR > 1 assumption, and to segregate areas representing volume scattering in relatively smooth surfaces inside anomalous craters, where possibility of dielectric mixing (or water-ice) may be high.

Critical Analysis of Model-Based Incoherent Polarimetric Decomposition Methods and Investigation of Deorientation Effect

This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power.

Explicating MiniSAR data to underline significant properties of lunar surface

There have been many investigations regarding water-ice depositions on the lunar surface and it is always been challenging. The previous studies were based on the circular polarization ratio (CPR). However, the CPR has proved to be inefficient in making distinctive classification of smooth (water-ice) and rough surface. Therefore, instead of using single polarimetric parameter CPR, it is required to analyze the CPR>1, along with other significant physical and electrical properties for better textural classification. In this paper, we have established the relationship between icy region and rough region based on physical property that is surface roughness measured with the help of fractal dimension method (‘D’) and electrical properties like real part of dielectric constant (ε′), imaginary part of dielectric constant (ε″), real (n) and imaginary (k) part of refractive index, skin depth (d) and reflectivity (R). The whole investigation indicates that the textural classification of the lunar surface with the help of physical and electrical properties gives superior results as compared to the single polarimetric parameter CPR.

Pattern analysis of MiniSAR data for differentiation of icy craters in lunar surface

Classification of water ice region on lunar surface with Mini-SAR data is quite challenging. Therefore, a probability density function (pdf) based pattern analysis approach has been applied to classify lunar surface. This paper represents the pattern analysis approach to fit data points to a distribution function for understanding the distribution behaviour of Mini-SAR data which helps in developing a method based on density functions to differentiate two types of craters namely icy (type-I) and non-icy (type-II) craters. Circular polarization ratio (CPR) is a very important parameter in study of lunar surface. More specifically, the criterion CPR>1 is used to determine possible presence of water-ice deposits on lunar surface So, its important to study distribution behaviour of CPR pixels and to determine best fitted distribution function representing this behaviour. Therefore, in this paper, pattern analysis techniques have been applied to differentiate two crater types based on the distribution behaviour of CPR. The best fitted function for CPR has been obtained as Generalized Extreme Value function which clearly differentiate type-I and type-II craters.

Design of a reconfigurable antenna with fractal geometry

The paper proposes design of a reconfigurable antenna incorporating Fractal Geometry. Sierpinski fractal geometry is implemented on a conventional Bow Tie Antenna to obtain a multiband response. In order to achieve reconfigurability, varactor diode has been used. Instead of using a plane uniform ground, Defected Ground Structure (DGS) has been used to improve radiation in the front direction. A comparison has been done to show the advantages of DGS. An evaluation of different parameters of traditional Bow Tie Antenna and Fractal Bow Tie Antenna has been presented to study the effects of Fractal geometry. The fractal geometry used not only lends a multiband characteristic to the proposed antenna, but also provides improved directivity, higher gain, etc. The results of the fabricated antenna conform to the simulated results by demonstration of single and multiband responses, which can be tuned to different configurations by varying the voltage across the varactor diodes.

Critical analysis of deorientation effect on various land covers: An application of POLSAR data

The aim of model based decomposition is to express coherency matrix in terms of various scattering components (like, volume, surface, double bounce, and helix). In spite of this decomposition, ambiguity occurs in scattering response from various land covers, like urban and vegetation. Deorientation process is believed to remove this ambiguity. However, there is a need to check whether decomposition methods and deorientation helps in identification of different land covers in terms of scattering mechanisms. To fulfil this task, in this paper, a study of four D decomposition methods with and without deorientation has been performed. The purpose of this study is to visualize the effect of deorientation on various land covers like, urban, vegetation, bare soil, water, and subsidence, in Jharia region, one of the major coal fields of India. Both visual and quantitative analysis have been performed for comprehensive evaluation of deorientation effect.