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Multisource data fusion


Applied research

 


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Researchers :

  • Stanislas de BÉTHUNE
  • Fabrice MULLER

In the framework of applied research projects in digital cartography and image processing, a new method of multiresolution image integration was developed by the laboratory SURFACES.

An important aim in the field of image integration techniques is to produce color composites combining the information of high spatial resolution satellite images with the multispectral information content of much lower spatial resolution satellite images. Both the essential spatial information of the high resolution image and the spectral information content of the low resolution channels have to be preserved, so as to produce pseudo high resolution spectral channels which can be more easily interpreted or further processed for improved classification or for other information extraction purposes.

The methodology uses adaptive image filtering techniques, equalizing the local mean and variance values of the high resolution image to those of a lower resolution channel. The resulting high resolution image still possesses its high structural information content while having acquired at a local scale the spectral characteristics of the low resolution channel.

In order to merge a high spatial resolution image with three lower resolution multispectral channels, these channels are first registered to the high resolution image and resampled to the same pixel size. The high resolution image is then merged separately with the three channels, and the combination of the three resulting upgraded channels allows to produce the desired color composite wich shows only minimal distortion of the original multispectral values while being strongly enriched in spatial information content.

High resolution and low resolution satellite images are often separately available for a given study area. The production of merged multiresolution images provides the potentential customers with enhanced image data allowing an improved interpretation analysis for map updating and other spatial analysis applications.

 

Methodology :

The multiresolution merging of a high spatial resolution image with a low spatial resolution channel tending to preserve the spectral characteristics of the low resolution channel is performed in two steps :

First, the low resolution channel is registered to the high resolution image and resampled to the same pixel size (geometric correction).

Next, the high resolution image is adaptively filtered so that the local means and variances measured within a moving window are adjusted to the corresponding local means and variances of the low resolution channel . This procedure tends to produce intensity matching of the high resolution image to the actual intensity values of the low resolution channel.

The general Local Mean Variance Matching - LMVM - algorithm is used to integrate two images, a high resolution image (H) into a low resolution channel (L) resampled to the same size as H.

The algorithm produces a simulated high spatial resolution image (F) pertaining the spectral characteristics of the low resolution channel (L). The small intensity differences between the merged image (F) and the original low resolution channel corresponds to the structural information content of the high resolution image. How well the spectral values are preserved will depend on the size of the filtering window. Small window sizes produce the least distortion. Larger filtering windows incorporate more structural information from the high resolution image, but with more distortion of the spectral values.

In order to produce spatially enhanced color composites, the high spatial resolution image is merged by the LMVM filter to the three selected low resolution spectral channels separately. The resulting images are then combined to produce the spatially enhanced color composite.

 

Results :

This methodology has been successfully applied for merging SPOT Panchromatic (10 m) with SPOT XS (20 m) data, KOSMOS KVR 1000 (5 m) with SPOT XS (20 m) data and, more recently, for merging IRS-1C Panchromatic (5 m) and Multispectral (25 m ) images.

 

IRS-1C Panchromatic IRS-1C Multispectral Merged image
Figure 1a. IRS-1C Panchromatic. Figure 1b. IRS-1C Multispectral. Figure 1c. Merged image.


IRS-1C Panchromatic IRS-1C Multispectral Merged image
Figure 2a. IRS-1C Panchromatic. Figure 2b. IRS-1C Multispectral. Figure 2c. Merged image.

 

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Date de création de la page : non précisée – Dernière modification : 12-01-2008 .