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Utilizing Multi-Resolution Image data vs. Pansharpened Image data for Change Detection

Utilizing Multi-Resolution Image data vs. Pansharpened Image data for Change Detection. V. Vijayaraj , C.G. O’ Hara & N.H. Younan GeoResources Institute , Mississippi State University. Introduction Change Detection Pansharpening Change Detection Approaches

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Utilizing Multi-Resolution Image data vs. Pansharpened Image data for Change Detection

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  1. Utilizing Multi-Resolution Image data vs. Pansharpened Image data for Change Detection V. Vijayaraj , C.G. O’ Hara & N.H. YounanGeoResources Institute , Mississippi State University

  2. Introduction Change Detection Pansharpening Change Detection Approaches Case Study using QuickBird Imagery and eCognition Software Conclusions Outline

  3. Introduction The use of high resolution imagery to update and maintain spatial databases has increased. Developing efficient automated change detection techniques to extract map accurate change features from coregistered multitemporal, multiresolution imagery has been an area of growing research interest. A change detection approach to extract changed urban features (Ex: new roads, new buildings) using object based processing, spatial contextual information and data fusion technique is presented.

  4. Change Detection Change detection involves the analysis of coregistered images taken at two different times for the same geographical area. The techniques can be grouped into • Supervised Change Detection • Change features are extracted by analyzing images • Classified using supervised classification. • Unsupervised Change Detection • Change features are extracted by analyzing the difference • images. There are different approaches to analyzing difference • images.

  5. Pansharpening is a pixel level data fusion technique used to increase the spatial resolution of the multispectral image using panchromatic image while simultaneously preserving the spectral information. Also known as resolution merge, image integration and multisensor data fusion. Applications Pansharpening • Sharpen multispectral data • Enhance features using complementary information • Enhance the performance of change detection algorithms using multi-temporal data sets • Improve Classification accuracy

  6. IHS sharpening Brovey sharpening Statistical regression model sharpening High pass filter sharpening PCA-based sharpening Wavelet-based sharpening Pansharpening … The spectral and spatial quality of the sharpened image should be analyzed before using the sharpened image for further applications. The spectral information in the pansharpened image should be more similar to the multispectral image while simultaneously an increase in the high detail information is desired.

  7. Some of the preprocessing steps are Coregistration, Radiometric normalization, Color transformation, and Spectral transformation. Image T1 Preprocessed Image T1 Thematic Classification T1 Land Cover/ Land Use Change Maps Post Classification Thematic Change Detection Image T2 Preprocessed Image T2 Thematic Classification T2 Change Detection Approaches • Post Classification Change Detection approach • ( Decision level change analysis)

  8. L.R. Image T1 Preprocessed L.R. Image T1 Polygons Indicating Probable Change Change cues, Indicators, Deltas Region Group Analysis L.R.Image T2 Preprocessed L.R. Image T2 Thematic Classification T1 Mask based on change cues Image T1 Preprocessed Image T1 Classification of Changed features Land Cover/ Land Use Change Maps Mask based on change cues Image T2 Change Detection Approaches • Pre Classification Change Detection approach • (Feature level change analysis)

  9. Image T1 Preprocessed Image T1 Classification of changed objects Based on features from T1 and T2 Multiresolution Segmentation into Image objects Land Cover/ Land Use Change Maps Image T2 Preprocessed Image T2 Change Detection Approaches • Object based Change Detection approach • (Object level change analysis using data fusion)

  10. A Case study was conducted using QuickBird imagery of Starkville, Mississippi. Case Study QuickBird Characteristics Spatial Resolution: Pan 0.6m MS 2.4 m Spectral bands: Pan: 450nm-900nm Blue: 450nm-520nm Green:520nm-600nm Red: 600nm-690nm NIR: 760nm-900nm Time Step1: Feb-2002 Time Step2: Mar-2004

  11. Multispectral image time1& time2 Multispectral Time 1 Multispectral Time 2

  12. Multispectral Image An area of interest – Multispectral time2

  13. Pansharpened Image An area of interest – Pansharpened time2

  14. eCognition an object oriented image analysis software was used for change detection. The multispectral and Pansharpened images at time2 were segmented into image objects based on scale, color, shape and compactness. Segmentation was not done on Time 1 image instead the object domain at time2 was used to drill down to images in time 1 and compare object features. Object based Approach

  15. The RGB- IHS color transform was performed and the transformed layers were also used. RGB- IHS setting :R= Green; G= Red ; B= NIR IHS Transformation

  16. Hue Difference: The hue Difference was thresholded to identify the new( changed) features (used to identify new urban features and water bodies) Hue Difference=Hue time2- Hue time1 Water Ratio: Water ratio was used to identify new water bodies inside the new features class domain Water Ratio= (Blue+Green) / NIR Spatial contextual information to add objects along the edge of water bodies to the appropriate class Hue: The highest 10% quantile of Mean Hue of the objects were used to identify other existing urban features in time2. Features

  17. NDVI: NDVI in time step 2 was used to classify vegetation NDVI= (NIR-Red)/(NIR+Red) NDVI was also used to identify cleared / barren areas Some of the urban features which were classified as cleared were reclassified based on their proximity to urban features. Water ratio: Water ratio was used to classify existing water bodies. Building shadows were also picked up as water were removed based on amount of relative border with other water objects Features …

  18. Hue Time1 Multispectral Hue Time 1 Pansharpened Hue Time 1

  19. Hue Time2 Multispectral Hue Time 2 Pansharpened Hue Time 2

  20. Hue Difference Multispectral Hue Difference Pansharpened Hue Difference

  21. Water Ratio Time1 Multispectral Water Ratio Time 1 Pansharpened Water Ratio Time 1

  22. Water Ratio Time2 Multispectral Water Ratio Time 2 Pansharpened Water Ratio Time 2

  23. Water Ratio Difference Multispectral Water Ratio Difference Pansharpened Water Ratio Difference

  24. NDVI Time1 Multispectral NDVI Time 1 Pansharpened NDVI Time 1

  25. NDVI Time2 Multispectral NDVI Time 2 Pansharpened NDVI Time 2

  26. NDVI Difference Multispectral NDVI Difference Pansharpened NDVI Difference

  27. Change Features Multispectral Changed Features Pansharpened Changed Features

  28. Multispectral Classification

  29. Pansharpened Classification

  30. Comparison Multispectral Pansharpened

  31. A Change detection approach using high resolution imagery, object based classification, spatial context information and data fusion techniques was illustrated. The Pansharpened images can be used to extract features that are not distinguishable in the multispectral image. The spectral and spatial quality of the sharpened image need to be analyzed before using them for classification and change detection. Conclusions

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