Remote sensing of earth terrain

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others

A Study on Utilization of Polarimetric SAR Data in Planning a Smart City

AbstractIn the present world, there is a huge requirement for a truly efficient city not only operating in an integrated mode, but also to optimize the resources of each system to have better eco-friendly livelihood. Currently, this novel concept has led to the establishment of smart city with integration among informational and operational efficiency. With recent advances in remote sensing especially in the field of Polarimetric Synthetic Aperture Radar (SAR) data, using suitable polarimetric target decomposition techniques, data can be classified for further utilization in remote sensing applications. As a part of this exploration, a study has been taken to understand the utilisation of polarimetric data in building a smart city by exploiting the available resources in a given urban area. Different types of polarimetric decomposition techniques are applied on the data along with polarimetric speckle filters where classification of targets is performed based on the scattering mechanism of the polarized wave with each target in the scene. Encouraging preliminary results were obtained in the study using polarimetric SAR data adding another dimension in planning a smart city

Levee Slide Detection using Synthetic Aperture Radar Magnitude and Phase

The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results

Detecting depolarized targets using a new geometrical perturbation filter

Target detectors using polarimetry are often focused on single targets, since these can be characterized in a simpler and deterministic way. The algorithm proposed in this paper is aimed at the more difficult problem of partial target detection (i.e. targets with arbitrary degree of polarization). The authors have already proposed a single target detector employing filters based on a geometrical perturbation. In order to enhance the algorithm to the detection of partial targets, a new vector formalism is introduced. The latter is similar to the one exploited for single targets but suitable for complete characterization of partial targets. A new feature vector is generated starting from the covariance matrix, and exploited for the perturbation method. Validation against L-band fully polarimetric airborne E-SAR, and satellite ALOS-PALSAR data and X-band dual polarimetric TerraSAR-X data is provided with significant agreement with the expected results. Additionally, a comparison with the supervised Wishart classifier is presented revealing improvements

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.

This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times