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Spectral Characteristics Preserving Fusion of Remotely Sensed Multi-sensor ImagesAuthor: Mayank Goyal Date: 2017-07-04 Report no: IIIT/TH/2017/30 Advisor:K S Rajan AbstractIn remote sensing, multi-sensor data fusion aims at combining strengths of different data sources in a manner such that the resultant fused product preserves the best of the participating sources. From among all the levels at which fusion is done, namely pixel level, feature level and decision/information level, pixel level data fusion is the most elementary form of fusion. The real advantage with pixel level fusion is that it has the actual raw input data at its disposal which could be transferred to the output in a form closest to the input. Hence, pixel level algorithms need to aim at preserving the input properties rather than just improving the overall visual quality. Most of the existing methods are aimed at visual quality improvement and perform fusion using the global distributions, which results in loss of uniqueness of certain objects features. Such objects are either completely lost or their responses are milder in the output. These kind of local distributions can be preserved using a localized method of fusion. Other limitations of fusion methods include distortion of overall spectral distribution of the input data source and their applicability on the type of data being fused. There is a Gaussian Linear Stretch Image (GLSI) technique [1] for images fusion to handle spectral distortion. It is a moving window-based distribution matching and details are transferred in a linear manner. We have enhanced this algorithm to effectively handle two major challenges it faced. One is the detection and matching of class distributions which are inverted in the low and high resolution images and the other being the fusion of pixels belong to class distributions with significantly low sample space. We call it the Enhanced Gaussian Linear Stretch Image (e-GLSI) algorithm. The proposed e-GLSI algorithm is also a window based technique which aims at improving spatial resolution of multispectral bands using higher resolution panchromatic band. e-GLSI fuses the two inputs by transferring the spatial details of the panchromatic band into each multispectral band independently at local window level, hence prevents suppression of local unique features and preserves the spectral distribution of each band as well. In this approach, the local window statistics are used to establish linear relationships between corresponding windows from two different spatial resolution bands. Cases of distribution inversions and unimodal splits are handled using well defined metrics before fusing two distributions together. The algorithm then enhances each band independently without using any derived or aggregated knowledge, thus making this technique suitable to fuse any combination of input images. Results show that the statistical profile of the e-GLSI output bands matches the original multispectral image bands’ statistical profile better than other widely used approaches. In order to evaluate this matching quantitatively, standard deviation of the relative difference in fused pixel value and original multispectral pixel value per band has been computed. e-GLSI output gives a standard deviation of 0.18, 0.12 and 0.26 for red, blue and green bands respectively. Thus showing that it’s able to preserve the spectral characteristics from input images. In Spite of being able to preserve the spectral characteristics of the input images, there are some artifacts introduced in the e-GLSI fusion output. One of them is objects getting split across the moving window of the technique, which introduces a window boundary or sharp edge in some of the objects. A way to handle this could be the use of a variable sized window depending on the area of the image being processed. But this in itself is a challenge as it would require surrounding information and certain heuristics in order to decide the window size at a location in an image. In order to tackle this challenge, we used objects as windows in our approach. We propose an object based fusion algorithm based on GLSI, which we call the Object based GLSI (o-GLSI). Processing blocks for this approach are are the objects segmented from the Pan image. Since pixels being fused are part of a single object, we transfer details in a Gaussian manner in case of o-GLSI. The quantitative evaluation of o-GLSI fusion results clearly show that o-GLSI standard deviation of 0.32, 0.49 and 0.82 for relative difference in fused pixel and ground truth multispectral pixel for red, blue and green bands respectively is successful in keeping the fusion output spectrally closer to the input. Universal Image Quality Index (UIQI) [2] values of 0.9 for each band with respect to the ground truth proves that the quality of image fused by o-GLSI is very good. Since classification is one of the prominent applications for fused images, a superior accuracy rate supports the effectiveness of the fusion algorithm involved. As another measure to evaluate the utility of the fused images, k-means classification accuracy has been computed and compared across various fusion algorithms. Classification accuracy depends on the trueness of the input images. Any tweakings in the spectral properties of the images as a result of fusion alrogithms directly translates to misclassification. This is evident from the classification done on the images fused by the proposed algorithm. Proposed method’s output shows a accuracy rate of 73%, while IHS, Brovey and PC based methods have shown a classification accuracy of 54%, 21% and 42% respectively. Full thesis: pdf Centre for Spatial Informatics |
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