• Title/Summary/Keyword: RFM Model

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A STUDY ON DEM GENE]RATON USING POLYNOMIAL CAMERA MODEL IN SATELLITE IMAGERY

  • Jeon, Seung-Hun;Kim, Sung-Chai;Lee, Heung-Jae;Lee, Kae-hei
    • Proceedings of the KSRS Conference
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    • 2002.10a
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    • pp.518-523
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    • 2002
  • Nowadays the Rational Function Model (RFM), an abstract sensor model, is substituting physical sensor models for highly complicated imaging geometry. But RFM is algorithm to be required many Ground Control Points (GCP). In case of RFM of the third order, At least forty GCP are required far RFM generation. The purpose of this study is to research more efficient algorithm on GCP and accurate algorithm similar to RFM. The Polynomial Camera Model is relatively accurate and requires a little GCP in comparisons of RFM. This paper introduces how to generate Polynomial Camera Model and fundamental algorithms for construction of 3-D topographic data using the Polynomial Camera Model information in the Kompsat stereo pair and describes how to generate the 3-D ground coordinates by manual matching. Finally we tried to extract height information for the whole image area with the stereo matching technique based on the correlation.

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Target Market Determination for Information Distribution and Student Recruitment Using an Extended RFM Model with Spatial Analysis

  • ERNAWATI, ERNAWATI;BAHARIN, Safiza Suhana Kamal;KASMIN, Fauziah
    • Journal of Distribution Science
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    • v.20 no.6
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    • pp.1-10
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    • 2022
  • Purpose: This research proposes a new modified Recency-Frequency-Monetary (RFM) model by extending the model with spatial analysis for supporting decision-makers in discovering the promotional target market. Research design, data and methodology: This quantitative research utilizes data-mining techniques and the RFM model to cluster a university's provider schools. The RFM model was modified by adapting its variables to the university's marketing context and adding a district's potential (D) variable based on heatmap analysis using Geographic Information System (GIS) and K-means clustering. The K-prototype algorithm and the Elbow method were applied to find provider school clusters using the proposed RFM-D model. After profiling the clusters, the target segment was assigned. The model was validated using empirical data from an Indonesian university, and its performance was compared to the Customer Lifetime Value (CLV)-based RFM utilizing accuracy, precision, recall, and F1-score metrics. Results: This research identified five clusters. The target segment was chosen from the highest-value and high-value clusters that comprised 17.80% of provider schools but can contribute 75.77% of students. Conclusions: The proposed model recommended more targeted schools in higher-potential districts and predicted the target segment with 0.99 accuracies, outperforming the CLV-based model. The empirical findings help university management determine the promotion location and allocate resources for promotional information distribution and student recruitment.

RFM for High Resolution Satellite Sensor Modeling (RFM을 이용한 고해상도 인공위성 센서모델링)

  • 조우석;이동구
    • Korean Journal of Remote Sensing
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    • v.18 no.6
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    • pp.337-344
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    • 2002
  • In general, in order to obtain position information from satellite images, satellite sensor model which represents the geometric relationship between sensor and targeted area should be established in the first place. However, it is not simple for modelling pushbroom satellite sensor due to the image capturing process. In recent development of new generation imaging sensors, a generic sensor model, which is applicable to all types of sensors such as frame, pushbroom, whiskbroom, and SAR is in great need to the remote sensing and photogrammetry community. In this paper, the RFM as sensor model was implemented with KOMPSAT EOC and SPOT satellite images and analyzed in cases where the number and distribution of ground control points were varied. The test results of RFM were presented and compared with those of Direct Linear Transformation(DLT).

An empirical study on RFM-T model for market performance of B2B-based Technology Industry Companies (B2B 중심의 기술 산업 기업의 수익성 성과를 위한 RFM-T 모형 실증 연구)

  • Miyoung Woo;Young-Jun Kim
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.2
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    • pp.167-175
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    • 2024
  • Due to the Fourth Industrial Revolution, ICT(Information and Communication Technology) industry is becoming more important and sophisticated than ever. In B2B based ICT industry demand forecasting by analyzing the previous customer data is so important. RFM, one of customer relationship management models is a marketing technique that evaluates Recency, Frequency and Monetary value to predict customers behavior. RFM model has been studied focusing on the B2C based industry. On the other hand there is a lack of research on B2B based technology industry. Therefore this study applied it to B2B based high technology industry and considered T(technology collaboration) value, which are identified as important factors in the technology industry. To present an improved model for market performance in B2B technology industry, an empirical study was conducted on comparing the accuracy of the traditional RFM model and the improved RFM-T model. The objective of this study is to contribute to market performance by presenting an improved model in B2B based high technology industry.

Proposal Methodology for Disaster Risk Analysis by Region Using RFM Model (RFM 모형을 활용한 지역별 재해 위험도 분석 방법론 제안)

  • Kim, TaeJin;Kim, SungSoo;Jeon, DaHee;Park, SangHyun
    • Journal of the Society of Disaster Information
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    • v.16 no.3
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    • pp.493-504
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    • 2020
  • Purpose: The purpose of this study is to propose an analytical methodology for selecting the priority of preventive projects in the course of carrying out disaster prevention projects that improve disaster-hazardous areas. Method: Data analysis was performed using RFM model which can divide data grade and perform target marketing based on Recency, Frequency, and Monetary. Result: The top 10% of the area with high RFM value was mainly in the East Sea and the South Sea coast, and the number of damage in private facilities was high. Conclusion: In this study, we used the RFM model to select the priority of disaster risk and to implement the regional disaster risk using GIS. These results are expected to be used as basic data for selecting priority project sites for disaster prevention projects and as basic data in the decision-making process for disaster prevention projects.

The Application of RFM for Geometric Correction of High-Resolution Satellite Image Data (고해상도 인공위성 영상데이터의 기하보정을 위한 RFM의 적용)

  • 안기원;임환철;서두천
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.20 no.2
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    • pp.155-164
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    • 2002
  • In this study, in order to discuss the geometric correction methods of high-resolution IKONOS satellite image, the existing polynomial model and RFM which is able to rectify satellite image without auxiliary data are applied to IKONOS satellite image data. Then the accuracy of ground point versus number of GCPs and each order of RFM are assessed. A numerical instability is removed by application of Tikhonov regularization method. As the results of this study, the root mean square errors of RFM is decreased more than 2 pixels in comparison with the two dimensional polynomial model.

A Study on RFM Based Stereo Radargrammetry Using TerraSAR-X Datasets (스테레오 TerraSAR-X 자료를 이용한 RFM 기반 Radargrammetry에 관한 연구)

  • Bang, SooNam;Koh, JinWoo;Yun, KongHyun;Kwak, JunHyuck
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.32 no.1D
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    • pp.89-94
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    • 2012
  • The RFM (Rational Function Model), as an alternative to physical sensor models has been widely used for photogrammetric processing of high resolution optical satellite imagery. However, the application of RF modeling to the SAR (Synthetic Aperture Radar) is very limited. In this paper, stereo radargrammetric processing of TerraSAR-X stereo pairs with RFM is implemented and analyzed. The investigation has shown that the accuracy of TerraSAR-X DSM is similar to that of the commercial S/W product. Finally, it is demonstrated that RFM is effective and feasible in the application to the radargrammetric SAR image processing.

Evaluation of The Image Segmentation Method for DEM Generation of Satellite Imagery (위성영상의 DEM 생성을 위한 영상분할 방법의 적합성 평가)

  • 이효성;송정헌;김용일;안기원
    • Korean Journal of Remote Sensing
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    • v.19 no.2
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    • pp.149-157
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    • 2003
  • In this study, for efficient replacement of sensor modelling of high-resolution satellite imagery, image segmentation method is applied to the test area of the SPOT-3 satellite imagery. After that, a third-order polynomial model in the sectioned area is compared with the RFM which Is to the entire in the test area. As results, plane error of the third-order polynomial model is lower(approximately 0.8m) than that of RFM. On the other hand, height error of RFM is lower(approximately 1.0m).

Accuracy of Precision Ground Coordinates Determination Using Inverse RPC in KOMPSAT Satellite Data (다목적실용위성(KOMPSAT)의 Inverse RPC 해석을 통한 정밀지상좌표 결정 정확도)

  • Seo, DooChun;Jung, JaeHun;Hong, KiByung
    • Aerospace Engineering and Technology
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    • v.13 no.2
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    • pp.99-107
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    • 2014
  • There are two types of Physical Model and RFM (Rational Function Model) is to determinate ground coordinates using KOMPSAT-2 and KOMPSAT-3 satellite data. Generally, RPCs(Rational Polynomial Coefficients) based on RFM is provided for users. This RPCs is to compute the ground coordinates to the image coordinates. If users produce ortho-image with provided RPCs is useful, directly compute the ground coordinates corresponding to image coordinates and check location accuracy etc. are difficult. In this study, a basic algorithm of inverse RPCs that calculates the image coordinates to ground coordinates, compute based on provided RPCs and evaluation of determinated ground coordinates using developed inverse RPCs were proposed.

The Application of Orbital Modeling and Rational Function Model for Ground Coordinate from High Resolution Satellite Data (고해상도 인공위성데이터로부터 지상좌표 결정을 위한 궤도모델링 및 RFM기법 적용)

  • Seo, Doo-Chun;Yang, Ji-Yeon;Lee, Dong-Han;Im, Hyo-Suk
    • Aerospace Engineering and Technology
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    • v.7 no.2
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    • pp.187-195
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    • 2008
  • Generation of accurate ground coordinates from high resolution satellite image are becoming increasingly of interest. The primary focus of this paper is to compute satellite direct sensor model (DSM) and rational function model (RFM) for accurate generation of ground coordinates from high resolution satellite images. Being based on this we presented an algorithm to be able to efficiently ground coordinates about large area with introducing RFM(rational function model) method applied to rigorous sensor modeling standing on basis of satellite orbit dynamics and collinearity equation, and sensor modeling of high-resolution satellite data like IKONOS, QuickBird, KOMPSAT-2 and others. The general high resolution satellite measures the position, velocity and attitude data of satellite using star, gyro, and GPS sensors.

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