Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Water Harvesting Structure Positioning by Using Geo-Visualization Concept and Prioritization of Mini-Watersheds Through Morphometric Analysis in the Lower Tapi Basin

Geo-visualization concept has been used for positioning water harvesting structures in Varekhadi watershed consisting of 26 mini watersheds, falling in Lower Tapi Basin (LTB), Surat district, Gujarat state. For prioritization of the mini watersheds, morphometric analysis was utilized by using the linear parameters such as bifurcation ratio (R_b), drainage density (D_d), stream frequency (F_u), texture ratio (T), length of overland flow (L_o) and the shape parameter such as form factor (R_f), shape factor (B_s), elongation ratio (R_e), compactness constant (C_c) and circularity ratio (R_c). The different prioritization ranks were assigned after evaluation of the compound factor. 3 Dimensional (3D) Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM) and DEM from topo contour were analyzed in ArcScene 9.1 and the fly tool was utilized for the Geo-visualization of Varekhadi mini watersheds as per the priority ranks. Combining this with soil map and slope map, the best feasibility of positioning check dams in mini-watershed no. 1, 5 and 24 has been proposed, after validation of the sites.     

A Hierarchical Hybrid SVM Method for Classification of Remotely Sensed Data

The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection of a flexible classifier with robust learning process. Experiments conducted revealed that a computationally cheap algorithm that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions. The proposed method’s overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging Spectroradiometer Indian Pines hyperspectral benchmark data set and presented.     

Recent advances in accessibility research: Representation,methodology and applications

 In this article we examine recent advances in accessibility research and their implications for future studies. We base our discussion on three intersecting dimensions that are useful for evaluating the contribution of recent studies: representation, methodology and applications. Various examples are selected to show that research concerned with representation and methodological problem solving is often applied to issues of broad concern in policy and planning. It is, however, not clear that the simultaneous treatment of representation, methodological and application issues has ever been fully worked out. The questions raised in this article may serve as a foundation for addressing issues pertinent to accurate representation, improved model building, and more rigorous applications in accessibility research. Received: 9 December 2002 / Accepted: 10 February 2003     

PLL Tracking Performance in the Presence of Oscillator Phase Noise

The tracking performance of a Phase Lock Loop (PLL) is affected by the influence of several error sources. In addition to thermal noise and dynamic stress error, oscillator phase noise can cause significant phase jitter which degrades the tracking performance. Oscillator phase noise is usually caused by two different effects: Allan deviation phase noise is caused by frequency instabilities of the receiver's reference oscillator and the satellite's frequency standard. It can be termed as system-inherent phase noise and is relevant for both static and dynamic applications. “External” phase noise, however, is caused by vibration and is a major problem for dynamic applications. In the context of this paper, both types of phase noise will be modeled and the resulting integrals will be evaluated for PLLs up to the third order. Besides, phase jitter induced by thermal noise and signal dynamics will also be discussed, thus providing all necessary formulas for analyzing the performance of a phase lock loop in case of different forms of stress. Since the main focus is centered on the effects of oscillator phase noise, the overall PLL performance is graphically illustrated with and without consideration of oscillator phase noise. © 2002 Wiley Periodicals, Inc.     

Integration and Sharing of Geo-spatial Data Based on Data Engine

Through analyzing the principle of data sharing in the database system, this paper discusses the principle and method for integrating and sharing GIS data by data engine, introduces a way to achieve the high integration and sharing of GIS data on the basis of VCT in VC , and provides the method for uniting VCT into RDBMS in order to implement a spatial database with object-oriented data model.     

台湾集集大地震断层非均匀滑动分布的反演

利用台湾1999集集大地震前后GPS测量获得的地表位移数据,反演断层面上的非均匀滑动分布.反演中采用二次样条函数作为滑动分布的基函数,通过引入阿卡克贝叶斯准则,获得了反演问题的惟一解.反演得到的地表位移与实测位移的差在几个厘米,较好地解释了观测到的GPS位移.另一方面,用反演结果计算得到的地表形变场同INSAR得到的地表实测位移场较相近.     

Component-based topological data model for three-dimensional geology modeling

On the study of the basic characteristics of geological objects and the special requirement for computing 3D geological model, this paper gives an object-oriented 3D topologic data model. In this model, the geological objects are divided into four object classes: point, line, area and volume. The volume class is further divided into four subclasses: the composite volume, the complex volume, the simple volume and the component. Twelve kinds of topological relations and the related data structures are designed for the geological objects.