Objective PLDF quality assessment indices

2.2.1 Objective PLDF quality assessment indices

Due to the kind of distortions and the used datasets all properties of fused data must be measured. As the spectral and spatial properties of Pan and MS datasets always do not obey the likeness rules therefore both spectral and spatial resolutions of the fused images must be evaluated. Accordingly, using some quantitative indicators the performance of the used techniques with respect to the spectral and spatial information contents of the fused image in compare to the reference datasets are measured. These indicators are providing measures of the closeness between fused

and reference datasets by utilizing the differences in spectral and spatial statistical distributions of DNs.

With reference to this fact that the number of objective image quality assessment metrics are dramatically high and according to the availability of reference dataset (which the fused dataset is to be compared with [Wang et al. 2004]) the developed and applied objective indices are categorized into three main groups.

1. Full-reference image quality assessment techniques that use an available complete reference image.

2. No-reference or blind quality assessment approaches are desirable in cases which reference image is unavailable.

3. Reduced-reference in which the reference image is only partially available. In such a procedure a set of extracted features made available as side information to help in evaluation the quality of fused image.

As in this work the main objective is to evaluate the kind and amount of distortion due to the fusion process accordingly a quality assessment that can offer the whole information is desirable. Therefore here the focus is on full-reference quality assessments. As the reference datasets were not available for second property of Wald, therefore the needed reference datasets are produced.

2.2.1.1 Wald’s property indicators

Due to the main rational behind the PLDF procedures that aims at injecting the higher spatial resolution properties of Pan into the MS images while their original spectral content are preserved. Therefore the importance of spectral consistency is very high for almost all applications of satellite imagery especially those that are depend on the spectral properties of data e.g. classification and visual image interpretation. As mentioned above the number of indicators is high and very diverse that make the quality assessment be complicated. In order to make a thorough and global

measurement Wald et al. [1997] offered a standard protocol for evaluating quality of fused images. This protocol is formulated by three “properties” of fused images as:

1. Any MS fused image once downsampled to its original spatial resolution should be as identical as possible to the original image;

2. Any MS fused image band should be as identical as possible to the image band that a corresponding sensor would observe with the same high spatial resolution (if available);

3. The fused dataset should be as identical as possible to the dataset that a corresponding sensor would observe with the same high spatial resolution (if available).

4. Any fused image band must be as spatially informative as possible in compare to original panchromatic image.

As three first properties are mostly based on the spectral fidelity of the fused dataset and the spatial fidelity has a minority of importance therefore we added the last property as the complementary to Wald’s protocol. The last added property is return to the spatial homo- or heterogeneity of the fused image which is evaluated in compare to the original Pan image. This parameter helps to know the spatial information contents of the Pan and helps to know “are spatial information properly transferred into the fused dataset?

2.2.1.2 Wald’s requirements

In order to put the above-mentioned measurements into practice, an indicator must be used that can offer a measurement of similarity between fused image and a proper reference (if available). In this regard as a general framework Wald [2002] numbered three main “requirements” for any image quality assessment indicator.

1. It should be independent of data units, calibration coefficients and instrument gains. Consequently it must be applicable to unitless quantities of DNs or radiances and any other forms of data values.

2. This quantity should be independent from the number of spectral bands under consideration. This is a sine qua non condition to compare results obtained in various conditions.

3. This quantity should be independent of Sharpening Factor (SF) or the scales of Pan and MS datasets. This permits to compare results obtained in different scales and for different image resolutions.

2.2.1.3 Reference image creation

From the literature several methods have been proposed to make a reference image.

The routine for reference image creation is adapted from is based on image scale changing. In this procedure it is assumed that an artificially-made reference image quality evaluation can offer the needed quality measurements for assessing the real images:

1. ^{PanH ⎯Register⎯⎯⎯and⎯downsample⎯⎯⎯⎯to⎯ MS⎯resolution⎯⎯⎯→PanL}. 2. ^{MSL ⎯⎯Downsample⎯⎯⎯ ⎯tovery ⎯⎯low⎯resolution⎯⎯⎯→MSVL}.

3. L Pixelleveldatafusion k^HF

H MSk MS

Pan , ⎯⎯⎯⎯⎯⎯⎯⎯→ .

4. VL Pixelleveldatafusion k^LF

L MSk MS

Pan , ⎯⎯⎯⎯⎯⎯⎯⎯→ .

5. ^MSL^{, MS}k^{LF Statisticalandvisualcomparison Q measurement}

k ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯→ ulality .

6. Artificial quality measurement generalized as real quality measurement of the PLDF technique.

Where H, L and VL are the High (e.g. Pan, 10 m), Low (e.g. MS, 30 m) and Very Low (Downsampled MS, 90 m) spatial resolutions of real and downsampled datasets, respectively. The mentioned routine for making a reference image and generalize the

quality of the fabricated reference image (MS^VFk ) to the real image (MSk^L ) is based on the assumption that the error should increase with the enhancing of spatial resolution.

Since the complexities of a scene usually increase as the resolution is getting higher, therefore the above assumption can be reliable. As the amount of sharpening factor for VL to L datasets is the same as for L to H therefore the obtained accuracies using these routines are reliable and can be generalized to the real fused datasets. Regarding to the relationship between object and pixel sizes that will change based on resolution changes therefore it seems the assumption of scale changing is not a comprehensive assumption. But from the literature this procedure is the most acceptable one and has been adapted in this work.