A novel SLC to GEO image generation approach and automatic control points conversion from GEO to SLC domain for SAR data quality evaluation and analysis

This paper presents a novel and efficient approach for the generation of georeferenced (GEO) image from Single Look Complex (SLC) image and a procedure for automatic conversion of control points from GEO to SLC image. Using the proposed approach, the control points in one image can be transformed to the corresponding control points in other image with subpixel accuracy. Automatic transformation of control points from GEO to SLC domain is an essential requirement in many of the Geometric Data Quality Evaluation (GDQE) and Radiometric Data Quality Evaluation (RDQE) activities. One of the main reason for control point transformation is that it is difficult to identify control points in SLC image due to its orientation, distortion and geometry of slant range SAR data. The proposed approach is also made operational in ISRO’s Data Quality Evaluation (DQE) software and is extensively applied for GDQE/RDQE evaluations on SAR data products

Intervention analysis with nonlinear dependent noise variation

Investigations of nonlinear fittings in combination with interventions have not been found in the literature so far. Because of the complexities of the procedures and intractability of the model identification and estimation of parameters of nonlinear models, the study has not developed. Recently the authors of this work developed a technique to fit a time series using a nonlinear model called the Quadratic Volterra Type (QVT) model (see Sarkar and Kartikeyan, 1987). Its methodology is quite tractable and easily amenable to the present context where nonlinearity is a dominant factor in studying the impact of interventions. We present methods of studying such nonlinear time series with three different kinds of intervention. Examples with naturally occurring series and with simulated data are presented to illustrate our techniques.Intervention analysis Volterra model AIC.

NOISE REMOVAL TECHNIQUES FOR MICROWAVE REMOTE SENSING RADAR DATA AND ITS EVALUATION

Microwave Remote Sensing data acquired by a RADAR sensor such as SAR(Synthetic Aperture Radar) is affected by a peculiar kind of noise called speckle. This noise not only renders the data ineffective for classification, texture analysis, segmentation etc. which are used for image analysis purposes, but also degrades the overall contrast and radiometric quality of the image. Here we discuss the various noise removal techniques which have been widely used by scientists all over the world. Different filtering methods have their pros and cons, and no single method can give the most satisfactory result. In order to circumvent those issues, better and better methods are being attempted. One of the recent methods is that based on Wavelet technique. This paper discusses the denoising techniques based on Wavelets and the results from some of those methods. The relative merits and demerits of the filters and their evaluation is also done

Vocal cord schwannoma: A rare case report

Schwannomas of the larynx are rare benign tumors, most commonly involving aryepiglottic folds or false vocal folds. When a tumor involves vocal cord, it causes clinical symptoms like hoarseness of voice and foreign body sensation. We report the CT and magnetic resonance imaging findings in a 19-year-old male patient with vocal cord mass histologically diagnosed as a vocal cord schwannoma

Vocal cord schwannoma: A rare case report

Schwannomas of the larynx are rare benign tumors, most commonly involving aryepiglottic folds or false vocal folds. When a tumor involves vocal cord, it causes clinical symptoms like hoarseness of voice and foreign body sensation. We report the CT and magnetic resonance imaging findings in a 19-year-old male patient with vocal cord mass histologically diagnosed as a vocal cord schwannoma