utilizing multi-resolution image data vs. pansharpened image data for change detection

ASPRS Annual Conference 2005 , Baltimore, March 09 2005

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


Outline

• Introduction• Change Detection• Pansharpening• Change Detection Approaches• Case Study using QuickBird Imagery and

eCognition Software• Conclusions


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.


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.


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


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.


Change Detection Approaches

•Post Classification Change Detection approach ( Decision level change analysis)

Image T2

Image T2Preprocessed

Image T2

Preprocessed Image T2

ThematicClassification T2


Image T1


Image T1




Post Classification Thematic Change

Detection

Post Classification Thematic Change

Detection

Land Cover/ Land Use Change Maps


Some of the preprocessing steps are Coregistration, Radiometric normalization, Color transformation, and Spectral transformation.



•Pre Classification Change Detection approach (Feature level change analysis)

L.R.Image T2

L.R.Image T2Preprocessed L.R. Image T2

Preprocessed L.R. Image T2

L.R. Image T1

L.R. Image T1Preprocessed L.R. Image T1

Preprocessed L.R. Image T1

Change cues, Indicators, Deltas

Change cues, Indicators, Deltas Region Group

Analysis

Region GroupAnalysis

Polygons Indicating

Probable Change

Polygons Indicating

Probable Change

Image T2

Image T2

Image T1


Image T1




Classification of Changed features

Classification of Changed features



Mask based on change cues






•Object based Change Detection approach (Object level change analysis using data fusion)

Image T2


Image T2


Image T1


Image T1


MultiresolutionSegmentation

into Image objects

MultiresolutionSegmentation

into Image objects

Classification of changed objects

Based on features from T1 and T2

Classification of changed objects

Based on features from T1 and T2




Case Study

A Case study was conducted using QuickBird imagery of Starkville, Mississippi.

QuickBird CharacteristicsSpatial Resolution: Pan 0.6m MS 2.4 mSpectral bands: Pan: 450nm-900nm Blue: 450nm-520nm

Green:520nm-600nm Red: 600nm-690nm NIR: 760nm-900nm

Time Step1: Feb-2002Time Step2: Mar-2004


Multispectral image time1& time2

Multispectral Time 1 Multispectral Time 2


Multispectral Image

An area of interest – Multispectral time2


Pansharpened Image

An area of interest – Pansharpened time2


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.


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


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.


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


Hue Time1

Multispectral Hue Time 1 Pansharpened Hue Time 1


Hue Time2

Multispectral Hue Time 2 Pansharpened Hue Time 2


Hue Difference

Multispectral Hue Difference Pansharpened Hue Difference


Water Ratio Time1

Multispectral Water Ratio Time 1 Pansharpened Water Ratio Time 1


Water Ratio Time2

Multispectral Water Ratio Time 2 Pansharpened Water Ratio Time 2


Water Ratio Difference

Multispectral Water Ratio Difference Pansharpened Water Ratio Difference


NDVI Time1

Multispectral NDVI Time 1 Pansharpened NDVI Time 1


NDVI Time2

Multispectral NDVI Time 2 Pansharpened NDVI Time 2


NDVI Difference

Multispectral NDVI Difference Pansharpened NDVI Difference


Change Features

Multispectral Changed Features Pansharpened Changed Features


Multispectral Classification


Pansharpened Classification


Comparison

MultispectralPansharpened


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.

utilizing multi-resolution image data vs. pansharpened image data for change detection

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