Dharmendra Singh

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.

A Radial Basis Function Approach to Retrieve Soil Moisture and Crop Variables from X-Band Scatterometer Observations

An outdoor crop-bed was prepared to observe scatterometer response in the angular range of 20◦ to 70◦ at VVand HHpolarization. The soil moisture and crop variables like plant height, leaf area index and biomass of crop ladyfinger were measured at different growth stages of the crop ladyfinger. Temporal variation in scattering coefficient was found highly dependent on crop variables and observed to increase with the increase of leaf area index and biomass for both polarizations. In this paper, a novel approach is proposed for the retrieval of soil moisture and crop variables using ground truth microwave scatterometer data and artificial neural network (ANN). Two different variants of radial basis function neural network (RBFNN) algorithms were used to approximate the function described by the input output relationship between the scattering coefficient and corresponding measured values of the soil moisture and crop variables. The new Corresponding author: R. Prasad (rprasad1@rediffmail.com). 202 Prasad, Kumar, and Singh model proposed in this paper gives near perfect approximation for all three target parameters namely soil moisture, biomass and leaf area index. The retrieval with minimal error obtained with the test data confirms the efficacy of the proposed model. The generalized regression network was observed to give minimal system error at a much lower spread constant.

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.

Military target detection using spectrally modeled algorithms and independent component analysis

Abstract. Most military targets of strategic importance are very small in size. Though some of them may get spatially resolved, most cannot be detected due to lack of adequate spectral resolution. Hyperspectral data, acquired over hundreds of narrow contiguous wavelength bands, are extremely suitable for most military target detection applications. Target detection, however, still remains complicated due to a host of other issues. These include, first, the heavy volume of hyperspectral data, which leads to computational complexities; second, most materials in nature exhibit spectral variability and remain unpredictable; and third, most target detection algorithms are based on spectral modeling and availability of a priori target spectra is an essential requirement, a condition difficult to meet in practice. Independent component analysis (ICA) is a new evolving technique that aims at finding components that are statistically independent or as independent as possible. It does not have any requirement of a priori availability of target spectra and is an attractive alternative. This paper, presents a study of military target detection using four spectral matching algorithms, namely, orthogonal subspace projection (OSP), constrained energy minimisation, spectral angle mapper and spectral correlation mapper, four anomaly detection algorithms, namely, OSP anomaly detector (OSPAD), Reed–Xiaoli anomaly detector (RXD), uniform target detector (UTD), a combination of RXD–UTD. The performances of these spectrally modeled algorithms are then also compared with ICA using receiver operating characteristic analysis. The superior performance of ICA indicates that it may be considered a viable alternative for military target detection.

An approach to determine possible existence of water ice deposits on lunar craters using minisar data

The present paper deals with the task of identifying lunar craters having possible existence of water-ice deposits on their surface. For this purpose, a decision tree algorithm has been proposed, in which decision criterion are decided on the basis of CPR, m-δ decomposition, fractal dimension `D and conditions proposed by Thompson et al., The proposed algorithm is successfully applied on Chandrayaan-1s MiniSAR data.

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.

Study and characterization of lunar craters using Mini-SAR data of Chandrayaan-1

Analyzing lunar surface with various approaches is still a very challenging task. Therefore, in this paper, an attempt has been made to critically analyze the lunar surface by using various approaches so that the possibility of water ice can be predicted. The polarimetric approach helps in determining physical and electrical properties of target. The polarimetric approaches, `m-δ decomposition and `m-χ decomposition, have been used for obtaining the information of scattering mechanisms. In order to determine electrical properties of lunar surface, dielectric constant has been measured using Campbells approach. Then, specular diffuse model has been applied for identifying various craters with reference to surface roughness and possibility of water-ice deposits.

Potential application of Kanade–Lucas–Tomasi tracker on satellite images for automatic change detection

Abstract. Monitoring agricultural areas is still a very challenging task. Various models and methodologies have been developed for monitoring the agricultural areas with satellite images, but their practical applicability is limited due to the complexity in processing and dependence on a priori information. Therefore, in this paper, an attempt has been made to investigate the utility of the Kanade–Lucas–Tomasi (KLT) tracker, which is generally useful for tracking objects in video images, for monitoring agricultural areas. The KLT tracker was proposed to deal with the problem of image registration, but the use of the KLT tracker in satellite images for land cover monitoring is rarely reported. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data has been used to identify and track the agricultural areas. The tracked pixels were compared with the agriculture pixels obtained from a decision tree algorithm and both results are closely matched. An image differencing change detection technique has been applied after KLT tracker implementation to observe the “change” and “no change” pixels in agricultural areas. It is observed that two kinds of changes are being detected. The areas where agriculture was not there earlier, but now is present, the changes are called positive changes. In the areas where agriculture was present earlier, but now is not present, those changes are referred to as negative changes. Unchanged areas retrieved from both the images are labeled as “no change” pixels. The novelty of the proposed algorithm is that it uses a simplified version of the KLT tracker to efficiently select and track the agriculture features on the basis of their spatial information and does not require a priori information every time.