ASPRS Annual Conference 2005, Baltimore, March 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

ASPRS Annual Conference 2005, Baltimore, March Outline Introduction Change Detection Pansharpening Change Detection Approaches Case Study using QuickBird Imagery and eCognition Software Conclusions

ASPRS Annual Conference 2005, Baltimore, March 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.

ASPRS Annual Conference 2005, Baltimore, March 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.

ASPRS Annual Conference 2005, Baltimore, March Pansharpening 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 Sharpen multispectral data Enhance features using complementary information Enhance the performance of change detection algorithms using multi-temporal data sets Improve Classification accuracy

ASPRS Annual Conference 2005, Baltimore, March Pansharpening … IHS sharpening Brovey sharpening Statistical regression model sharpening High pass filter sharpening PCA-based sharpening Wavelet-based sharpening 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.

ASPRS Annual Conference 2005, Baltimore, March Change Detection Approaches Post Classification Change Detection approach ( Decision level change analysis) Image T 2 Preprocessed Image T 2 Preprocessed Image T 2 Thematic Classification T 2 Thematic Classification T 2 Image T 1 Preprocessed Image T 1 Preprocessed Image T 1 Thematic Classification T 1 Thematic Classification T 1 Post Classification Thematic Change Detection Post Classification Thematic Change Detection Land Cover/ Land Use Change Maps Land Cover/ Land Use Change Maps Some of the preprocessing steps are Coregistration, Radiometric normalization, Color transformation, and Spectral transformation.

ASPRS Annual Conference 2005, Baltimore, March Change Detection Approaches Pre Classification Change Detection approach (Feature level change analysis) L.R.Image T 2 Preprocessed L.R. Image T 2 Preprocessed L.R. Image T 2 L.R. Image T 1 Preprocessed L.R. Image T 1 Preprocessed L.R. Image T 1 Change cues, Indicators, Deltas Change cues, Indicators, Deltas Region Group Analysis Region Group Analysis Polygons Indicating Probable Change Polygons Indicating Probable Change Image T 2 Image T 1 Preprocessed Image T 1 Preprocessed Image T 1 Thematic Classification T 1 Thematic Classification T 1 Classification of Changed features Classification of Changed features Land Cover/ Land Use Change Maps Land Cover/ Land Use Change Maps Mask based on change cues Mask based on change cues Mask based on change cues Mask based on change cues

ASPRS Annual Conference 2005, Baltimore, March Change Detection Approaches Object based Change Detection approach (Object level change analysis using data fusion) Image T 2 Preprocessed Image T 2 Preprocessed Image T 2 Image T 1 Preprocessed Image T 1 Preprocessed Image T 1 Multiresolution Segmentation into Image objects Multiresolution Segmentation into Image objects Classification of changed objects Based on features from T 1 and T 2 Classification of changed objects Based on features from T 1 and T 2 Land Cover/ Land Use Change Maps Land Cover/ Land Use Change Maps

ASPRS Annual Conference 2005, Baltimore, March Case Study A Case study was conducted using QuickBird imagery of Starkville, Mississippi. 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

ASPRS Annual Conference 2005, Baltimore, March Multispectral image time1& time2 Multispectral Time 1Multispectral Time 2

ASPRS Annual Conference 2005, Baltimore, March Multispectral Image An area of interest – Multispectral time2

ASPRS Annual Conference 2005, Baltimore, March Pansharpened Image An area of interest – Pansharpened time2

ASPRS Annual Conference 2005, Baltimore, March Object based Approach 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.

ASPRS Annual Conference 2005, Baltimore, March IHS Transformation The RGB- IHS color transform was performed and the transformed layers were also used. RGB- IHS setting :R= Green; G= Red ; B= NIR

ASPRS Annual Conference 2005, Baltimore, March Features 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.

ASPRS Annual Conference 2005, Baltimore, March Features … 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

ASPRS Annual Conference 2005, Baltimore, March Hue Time1 Multispectral Hue Time 1Pansharpened Hue Time 1

ASPRS Annual Conference 2005, Baltimore, March Hue Time2 Multispectral Hue Time 2Pansharpened Hue Time 2

ASPRS Annual Conference 2005, Baltimore, March Hue Difference Multispectral Hue DifferencePansharpened Hue Difference

ASPRS Annual Conference 2005, Baltimore, March Water Ratio Time1 Multispectral Water Ratio Time 1Pansharpened Water Ratio Time 1

ASPRS Annual Conference 2005, Baltimore, March Water Ratio Time2 Multispectral Water Ratio Time 2Pansharpened Water Ratio Time 2

ASPRS Annual Conference 2005, Baltimore, March Water Ratio Difference Multispectral Water Ratio DifferencePansharpened Water Ratio Difference

ASPRS Annual Conference 2005, Baltimore, March NDVI Time1 Multispectral NDVI Time 1Pansharpened NDVI Time 1

ASPRS Annual Conference 2005, Baltimore, March NDVI Time2 Multispectral NDVI Time 2Pansharpened NDVI Time 2

ASPRS Annual Conference 2005, Baltimore, March NDVI Difference Multispectral NDVI DifferencePansharpened NDVI Difference

ASPRS Annual Conference 2005, Baltimore, March Change Features Multispectral Changed FeaturesPansharpened Changed Features

ASPRS Annual Conference 2005, Baltimore, March Multispectral Classification

ASPRS Annual Conference 2005, Baltimore, March Pansharpened Classification

ASPRS Annual Conference 2005, Baltimore, March Comparison Multispectral Pansharpened

ASPRS Annual Conference 2005, Baltimore, March Conclusions 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.