1 / 13

Evaluation of user-oriented attractiveness of imaging spectroscopy data - a RSL case study -

Evaluation of user-oriented attractiveness of imaging spectroscopy data - a RSL case study -. Jens Nieke , Klaus Itten (RSL) and 10 MSc Geography Students. Outline of the Talk. Value Benefit Analysis (VBA) Value Benefit Analysis for Hyperspectral Data Objective Evaluation (Zielbewertung)

gratia
Download Presentation

Evaluation of user-oriented attractiveness of imaging spectroscopy data - a RSL case study -

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluation of user-oriented attractiveness of imaging spectroscopy data- a RSL case study - Jens Nieke, Klaus Itten (RSL) and 10 MSc Geography Students

  2. Outline of the Talk Value Benefit Analysis (VBA) Value Benefit Analysis for Hyperspectral Data Objective Evaluation (Zielbewertung) Objective Return (Zielertragsmatrix) Examples of Results Next Steps

  3. Value Benefit Analysis Goals of a Value Benefit Analysis*: (method applied in operations research and politics) • analysis of user needs • compare alternatives • prepare decision-making • unveil the decision process * Christof Zangemeister: Nutzwertanalyse in der Systemtechnik - Eine Methodik zur multidimensionalen Bewertung und Auswahl von Projektalternativen, 1974 (ISBN 3-923-26400-3)

  4. VBA • Procedure: • Compilation of a multi-dimensional objective system • Evaluation of objective system by experts / users • => objective value (of users) • Description of the related alternatives using the objective system • => objective return (of sensor data) • Value sythesis of users values and sensor data return Methodology multi-dimensional objective model for Hyperspectral Data Objective Model evaluated by experts => objective value Hyperspectral Imager Data Survey => objective return Comparison on level of objective value indicators Synthesis of values for each data type for various applications

  5. Objective Model for Hyperspectral Data 1. Definition of the Main Objective: “Max. attractiveness of hyperspectral data” 2. Definition of properties of hyperspectral data 3. Development of a hierarchical structure in form of a tree diagram with main objective (top) and subordinated objectives (low): A: Best image based properties A1: Best spectral parameters A2: Best spatial parameters A3:… B: Best ergonomic properties C: Low costs D: Best service 3. Lowest level of an objective model are the objective indicators * in function of user interest, applications etc.

  6. Mathematical Model • Law of Comparative Judgement • Weights of all objective on one level = 1 • Each alternative in evaluated using a target value • Completeness of the preference order Literature: Torgerson; Therory of methods and scaling; Wiley & Sons (1954) Keeney & Raiffa; Decisions with Multiple Objectives; Preferences and Value Tradeoffs; Wiley & Sons (1976 )

  7. Testing in 2006 Remote-Sensing Seminar • In SS2006, a group of 10 students tested the VBA approach for the HYRESSA project: • Development of an objective model for hyperspectral data • Testing of the VBA with • Dedicated experts from the field of vegetation/ limnology/landuse/geology • Dedicated sensors, i.e., CHRIS, HYPERION, ARES, AVIRIS, CASI, APEX • First analysis, evaluation and results (however, results are not significant* for general presentation) * only 4 experts and many assumptions for hyperspectral data returns

  8. Objective Model (1)

  9. Objective Model (2)

  10. Objective Model (3)

  11. Results of the Case-Study Results of the Case-Study: After evaluation of the objective model by experts and the description using the properties of hyperspectral data a value sythesis was performed: Results not representative, since no significance of the case study!

  12. Conclusion The case study showed, that a VBA is a suitable method • to analyse needs of hyperspectral data • to support sensor/data specification-building process Advantage: • Easy and clear handling • Comprehensive evaluation Disadvantage: • Efforts • Non-availability of sensor data parameters

  13. Next Steps Questionnaire (multi-dimensional objective system) to be discussed during the SWOT workshop

More Related