Kyung-Ok Kim

Tracking Road Centerlines from High Resolution Remote Sensing Images by Least Squares Correlation Matching

This paper describes a semi-automatic algorithm for tracking road centerlines from satellite images at 1 m resolution. We assume that road centerlines are visible in the image and that among points on road centerlines similarity transformation holds. Previous approaches proposed for semi-automatic road extraction include energy minimization and template matching with global enforcement. In this paper we will show that least squares correlation matching alone can work for tracking road centerlines. Our algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. An Ikonos image over Seoul area was used for test. The algorithm could successfully extract road centerlines once valid input points were provided from a user. The contribution of this paper is that we proved template matching could offer wider applicability in feature extraction, and we designed a new template matching scheme that worked for feature extraction without global enforcements.

Comprehensive Analysis of Sensor Modeling Alternatives for High Resolution Imaging Satellites

High-resolution imaging satellites are a valuable and cost effective data acquisition tool for a variety of mapping and GIS applications such as topographic mapping, map updating, orthophoto generation, environmental monitoring, and change detection. Sensor modeling that describes the mathematical relationship between corresponding scene and object coordinates is a prerequisite procedure prior to manipulating the acquired imagery from such systems for mapping purposes. Rigorous and approximate sensor models are the two alternatives for describing the mathematics of the involved imaging process. The former explicitly involves the internal and external characteristics of the imaging sensor to faithfully represent the geometry of the scene formation. On the other hand, approximate modeling can be divided into two categories. The first category simplifies the rigorous model after making some assumptions about the system’s trajectory and/or object space. Gupta and Hartley’s model, parallel projection, self-calibrating direct linear transformation, and modified parallel projection are examples of this category. Other approximate models are based on empirical formulation of the scene-to-ground mathematical relationship. This category includes among others, the well-known Rational Function Model (RFM). This paper addresses several aspects of sensor modeling. Namely, it deals with the expected accuracy from rigorous modeling of imaging satellites as it relates to the number of available ground control points, comparative analysis of approximate and rigorous sensor models, robustness of the reconstruction process against biases in the available sensor characteristics, and impact of incorporating multi-source imagery in a single triangulation mechanism. Following a brief theoretical background, these issues will be presented through experimental results from real datasets captured by satellite and aerial imaging platforms.

Epipolar Resampling of Space-borne Linear Array Scanner Scenes Using Parallel Projection

Epipolar resampling aims at generating normalized images where conjugate points are located along the same row. Such a characteristic makes normalized imagery important for many applications such as automatic image matching, aerial triangulation, DEM and ortho-photo generation, and stereo-viewing. Traditionally, the input media for the normalization process are digital images captured by frame cameras. These images could be either derived by scanning analog photographs or directly captured by digital cameras. Current digital frame cameras provide smaller format imagery compared to those of analog cameras. In this regard, linear array scanners are emerging as a viable substitute to two-dimensional digital frame cameras. However, linear array scanners have more complex imaging geometry than that of frame cameras. In general, the imaging geometry of linear array scanners produces non-straight epipolar lines. Moreover, epipolar resampling of captured scenes according to the rigorous model, which faithfully describes the imaging process, requires the knowledge of the internal and external sensor characteristics as well as a Digital Elevation Model (DEM) of the object space. Recently, parallel projection has emerged as an alternative model approximating the imaging geometry of high altitude scanners with narrow angular field of view. In contrast to the rigorous model, the parallel projection model does not require the internal or the external characteristics of the imaging system and produces straight epipolar lines. In this paper, the parallel projection equations are modified for better modeling of linear array scanners. The modified parallel projection model is then used to resample linear array scanner scenes according to epipolar geometry. Experimental results using Ikonos and SPOT data demonstrate the feasibility of the proposed methodology.

Automatic DEM generation using IKONOS stereo imagery

This paper introduces two methods for DEM generation in IKONOS stereo imagery using modified rational polynomial coefficients (RPC). The Rational Function Model (RFM) is the sensor model of IKONOS imagery for end-users. IKONOS imagery vendors provide RPC, RFM coefficients for IKONOS, for end-users with imagery. So it is possible that end-users obtain geospatial information in their IKONOS imagery without any additional effort. But there are still requirements for the rigorous DEM on RPC user. In IKONOS imagery, physical sensor modeling is difficult because IKONOS vendors do not provide satellite ephemeris data and abstract sensor modeling requires many ground control points (GCPs) well distributed in the whole image as well as other satellite imagery. Therefore RPC modification is the better choice. If a small number of GCPs is available, DEM can be generated using modified RPC by methods which are introduced in this paper. Study on DEM generation using modified RPC in IKONOS guarantees result reliability. Pseudo GCP generated with vendors RPC and additional GCP make it possible through sequential solution.

Semiautomatic Building Line Extraction from Ikonos Images Through Monoscopic Line Analysis

This paper proposes a new algorithm for extracting building lines from monoscopic high-resolution satellite images. We focus on extracting lines from rectangular-shaped building roofs with a relatively large size. We achieve this task by devising a unique approach using line voting and matching. Our algorithm works as follows. An input point on a building roof is given manually. A region of interest is defined centered on the input point and within the region lines extracted. Then, a line voting process is applied to estimate initial orientation and position of a building line. The orientation and position are refined by a least squares matching process. We assessed the performance of our algorithm against two Ikonos images. Our algorithm extracted building lines from over 83 percent of buildings tested with average angular accuracy of one or two degrees and average positional accuracy of approximately one pixel. This result supports the algorithm proposed in this paper. The major contribution of this paper is that we provided an alternative way to line grouping approaches when edge responses from buildings are not strong enough.

An efficient approach for service retrieval

The efficient discovery of services from a large-scale collection of services has become an important issue[1, 15]. We studied a pragmatic and efficient method for Web service retrieval. We regarded service retrieval as information retrieval on the proprietary XML formats, which are service descriptions in the registry DB. We enhanced the IR techniques to model and compute service similarity against queries on the off-the-shelf DBMS. Our technique considers not only UDDI data entries but also WSDL definitions of services. We also provide a technique to enhance performance of retrieval, considering structural features on the service descriptions.

Comprehensive comparisons among alternative sensor models for high resolution satellite imagery

Geometric modeling of satellite imagery is a prerequisite for many mapping and GIS applications. The more valid the sensor modeling is, the more accurate the end products are. Two main categories of sensor models exist; rigorous and approximate modeling. The former resembles the true geometry of the image formation procedure. Such a modeling requires the availability of the internal and external characteristics of the camera, which might not be always available. In addition, if these parameters are negatively affected by bias values, the accuracy of the rigorous model becomes questionable. Recently, there has been an increasing interest in approximate models, as they do not require the internal or external characteristics of the sensor. In this paper, a comparison between the rigorous and different approximate models is presented. Experimental results show the sensitivity of the rigorous model to bias values. Using an IKONOS dataset, it was found that the modified parallel projection model performs the best among all approximate models using a small number of control points. KeywordsSatellite Imagery; Rigorous Modeling; Approximate Modeling; Interior Orientation Parameters; Bias

The use of voting strategy for building extraction from high resolution satellite images

This paper proposes the use of voting strategy for extracting buildings from high resolution satellite images. Previously, the grouping strategy has been proposed and widely used for extraction of man-made features from images. In order to apply grouping we need to extract one complete line per each building side. However, this requirement may not be met for satellite images such as IKONOS images due to their spatial resolution. Often a long side of a building produces several lines with their orientation not necessarily identical. Lines from short side are often missing. In this situation we proposed to use a voting strategy. We vote line elements within a small region of interest for finding line position and orientation. The orientation and position are refined by a least squares matching process. We assessed the performance of our algorithm against an IKONOS image. Our algorithm extracted building lines from over 83% of buildings tested with an average angular accuracy of 2 degrees and average positional accuracy of 1.2 pixels. This promising result supports the use of voting strategy as proposed in this paper. Keywords-feature extraction; voting; IKONOS; line extraction

SSC-integrated TM mode LD for long-range surface-plasmon-polariton waveguide

We fabricated 1.3µm TM mode LD with integrated SSC. The high polarization ratio of 21dB was obtained between TM and TE mode. SSC supplied good coupling efficiency and alignment tolerance with LR-SPP waveguide. Clear eye opening was observed under 10Gbps NRZ modulation.

A Development of Change Detection System for Urban Monitoring

This paper presents the application system of remote sensing which monitors urban objects. The system mainly aims to help public officials detect the changed areas using easy method and friendly user interface. The land cover images and the ortho-rectified aerial images which have been linked together by using geo-coordinates information are the main data to be imported and processed in the system. The modules in the system have consisted of land cover change detection and urban change detection. The latter one is for urban monitoring and planning, vector data update. Especially, urban change detection module is very useful to reduce workload of public officials performing aerial image interpretation. To be more specific, an image differencing technique and change vector technique in conjunction with transparency viewing function are employed to detect the candidates of changed area and object. Change vector technique is based on the concept of spectral angle difference of pixels in image space between co-registered images from two dates. A final product is an image layer indicating location where change occurred by overlaying of digital thematic map. We mainly present the procedure regarding change detection run by Link Viewer module.