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Remote Sensing classifications

Pixel-oriented analysis. Object-oriented analysis. Remote Sensing classifications. Pixel-oriented limitations. Problems when dealing with rich information. Inappropriate scale of work. Inaccurate with elements of similar spectral behaviour (ex.: habitats). Salt and pepper effect.

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Remote Sensing classifications

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  1. Pixel-oriented analysis Object-oriented analysis Remote Sensing classifications

  2. Pixel-oriented limitations Problems when dealing with rich information Inappropriate scale of work Inaccurate with elements of similar spectral behaviour (ex.: habitats) Salt and pepper effect

  3. Which kind of structure can be seen in here? Object-oriented approach Important semantic information, necessary to interpret an image, is not represented in single PIXELS, but in meaningful OBJECTS , and their mutual relationships

  4. Segmentation Classification Procces characteristics Object parameters color stadistics texture shape & size context Allows scale definition Different scales Hierarchical system Multi source data fusion

  5. Image Segmented layer Classified layer Analysis workflow Multi-resolution segmentation Classification

  6. multispectral image (2.8 m, color) Pancromatic image (0.7m, b/w) Tematic information Example Objective: To study an urban settlement in Madrid (Spain) by means of remote sensing . Multi-source data fusion

  7. Example  Segmentation

  8. Example  Hierarchical classification

  9. Example  Hierarchical classification

  10. Example  Hierarchical classification

  11. Example  Hierarchical classification  Correct identification of roads, paths, houses, back-gardens, pools...

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