1 / 29

Computer Vision and Digital Photogrammetry Methodologies for Extracting Information and Knowledge from Remotely Sensed D

Computer Vision and Digital Photogrammetry Methodologies for Extracting Information and Knowledge from Remotely Sensed Data. Toni Schenk, CEEGS Department, OSU Bea Csatho, Department of Geology, University at Buffalo (formerly with BPRC, OSU). data specialist. data collection.

ata
Download Presentation

Computer Vision and Digital Photogrammetry Methodologies for Extracting Information and Knowledge from Remotely Sensed D

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. Computer Vision and Digital Photogrammetry Methodologies for Extracting Information and Knowledge from Remotely Sensed Data Toni Schenk, CEEGS Department, OSU Bea Csatho, Department of Geology, University at Buffalo (formerly with BPRC, OSU)

  2. data specialist data collection preprocessing raw data feature extraction geo scientist data/information analysis knowledge From Data to Knowledge effort diagram of geoscientist experience diagram

  3. data specialist data collection preprocessing raw data geospatial information provider information extraction geo scientist information analysis knowledge From Data to Information and Knowledge effort diagram of geoscientist experience diagrams

  4. perception data acquisition data From Data to Information and Knowledge in Human Vision knowledge scene information

  5. data acquisition data preprocessing raw data feature extraction information perceptual organization applications knowledge Paradigm

  6. knowledge semantic content information data signal representation symbolic Data, Information, Knowledge

  7. Data • sensor output, result of data acquisition • raw signals or preprocessed • data is not an end product, nor does it directly provide answers to applications • examples: pixels, laser points •  data needs to be (further) processed

  8. Information • more specific than data to answer questions • information can be retrieved from data  information implicitly in data • extracting information from data makes it explicit  feature extraction • features are information primitives

  9. Knowledge • elusive concept, ill-defined  means different things to different people • to ‘deal’ with knowledge it must be represented  knowledge and its representation are closely related • knowledge: facts, procedures, heuristics that can be used to make inferences

  10. Example of feature extraction and their perceptual organization • features are likely to correspond to object boundaries (pixels carry no explicit information about scene) • perceptual organization of features leads to structures that can be used to generate hypotheses about objects

  11. Test Site Ocean City Detail View Test Site

  12. Edge Detection

  13. Edge Segmentation segmentation of raw edges into straight lines threshold criteria straightness length from pixels to vectors

  14. Grouping:Rectangular Edges and Corners

  15. Edge Information • segmentation and grouping are perceptual processes • perceptual organization transforms data (edge pixels) into information (parallel edges, rectangular edges, corners,…) • edge information enters spatial reasoning process

  16. data information knowledge query From Data to Knowledge in a Multisensor Scenario

  17. Information and Knowledge Generating Processes

  18. Feature Extraction From Different Sensors • aerial/satellite imagery: • edges by edge operators • regions, e.g. by texture segmentation • LIDAR: • smooth surface patches, e.g. planar and higher order surfaces • multispectral/hyperspectral imagery: • regions with similar spectral properties

  19. Perceptual Organization of Laser Point Clouds • find structure in randomly distributed 3-D point clouds • feature extraction, e.g. planar surface patches • segmentation, merging • grouping: establish topological relationships, bare-earth separation, compute higher order features

  20. relationship? Fusion of Aerial Imagery and Lidar: Data Level image Lidar

  21. image Lidar raw edge region 1 region 2 relationship? Fusion of Aerial Imagery and Lidar: Extracted Features, 1st Level

  22. image Lidar segmented edge computed edge relationship = same physical phenomena Fusion of Aerial Imagery and Lidar: Extracted Features, 2nd Level region 1 region 2 sensor invariant feature

  23. aerial image aerial image Z Y laser points visible surface reference frame X Principle of Image Registration

  24. ICESat Laser Points Back-Projected To Stereo Images: Before Registration

  25. ICESat Laser Points Back-Projected To Stereo Images: After Registration

  26. Concluding Remarks • process of extracting useful information improves the ratio data gathering/knowledge creation • more data can be ‘mined’ • ‘useful’ information is application dependent

  27. Concluding Remarks • geoinformatics is multi-disciplinary • sensor designers, mission planners • data collection specialists • geoinformation providers • photogrammetry, remote sensing, computer vision, computer science • geoinformation managers (GIS) • geoscientists

More Related