Muhammad Murtaza Khan

Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments

Quality assessment of pansharpening methods is not an easy task. Quality-assessment indexes, like Q4, spectral angle mapper, and relative global synthesis error, require a reference image at the same resolution as the fused image. In the absence of such a reference image, the quality of pansharpening is assessed at a degraded resolution only. The recently proposed index of Quality Not requiring a Reference (QNR) is one among very few tools available for assessing the quality of pansharpened images at the desired high resolution. However, it would be desirable to cross the outcomes of several independent quality-assessment indexes, in order to better determine the quality of pansharpened images. In this paper, we propose a method to assess fusion quality at the highest resolution, without requiring a high-resolution reference image. The novel method makes use of digital filters matching the modulation transfer functions (MTFs) of the imaging-instrument channels. Spectral quality is evaluated according to Walds spectral consistency property. Spatial quality measures interscale changes by matching spatial details, extracted from the multispectral bands and from the panchromatic image by means of the high-pass complement of MTF filters. Eventually, we highlight the necessary and sufficient condition criteria for quality-assessment indexes by developing a pansharpening method optimizing the QNR spatial index and assessing the quality of fused images by using the proposed protocol.

Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

We propose to fuse the high spatial content of two 250-m spectral bands of the moderate resolution imaging spectroradiometer (MODIS) into its five 500-m bands using wavelet-based multiresolution analysis. Our objective was to test the effectiveness of this technique to increase the accuracy of snow mapping in mountainous environments. To assess the performance of this approach, we took advantage of the simultaneity between the advanced spaceborne thermal emission and reflection radiometer (ASTER) and MODIS sensors. With a 15-m spatial resolution, the ASTER sensor provided reference snow maps, which were then compared to MODIS-derived snow maps. The benefit of the method was assessed through the investigation of various metrics, which showed an improvement from 3% to 20%. Therefore, the enhanced snow map is of great benefit for environmental and hydrological applications in steep terrain.

Indusion: Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique

The fusion of multispectral (MS) and panchromatic (PAN) images is a useful technique for enhancing the spatial quality of low-resolution MS images. Liu recently proposed the smoothing-filter-based intensity modulation (SFIM) fusion technique. This technique upscales MS images using bicubic interpolation and introduces high-frequency information of the PAN image into the MS images. However, this fusion technique is plagued by blurred edges if the upscaled MS images are not accurately coregistered with the PAN image. In the first part of this letter, we propose the use of the Induction scaling technique instead of bicubic interpolation to obtain sharper, better correlated, and hence better coregistered upscaled images. In the second part, we propose a new fusion technique derived from induction, which is named ldquoIndusion.rdquo In this method, the high-frequency content of the PAN image is extracted using a pair of upscaling and downscaling filters. It is then added to an upscaled MS image. Finally, a comparison of SFIM (with both bicubic interpolation and induction scaling) is presented along with the fusion results obtained by IHS, discrete wavelet transform, and the proposed Indusion techniques using Quickbird satellite images.

Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction

This article presents a novel method for the enhancement of the spatial quality of hyperspectral (HS) images through the use of a high resolution panchromatic (PAN) image. Due to the high number of bands, the application of a pan-sharpening technique to HS images may result in an increase of the computational load and complexity. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. To solve this problem, we propose a pan-sharpening technique combining both dimensionality reduction and fusion, making use of non-linear principal component analysis (NLPCA) and Indusion, respectively, to enhance the spatial resolution of a HS image. We have tested the proposed algorithm on HS images obtained from CHRIS-Proba sensor and PAN image obtained from World view 2 and demonstrated that a reduction using NLPCA does not result in any significant degradation in the pan-sharpening results.

Decision-Based Fusion for Pansharpening of Remote Sensing Images

Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. A wide range of pansharpening methods are available, each producing images with different characteristics. To compare the performances and characteristics of different methods, a contest was held in 2006 by the IEEE Data Fusion Technical Committee. In this contest, À trous wavelet transform-based pansharpening (AWLP) and Laplacian pyramid-based context adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, we observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to design a method taking advantage of both methods by locally selecting the best one. This adaptive decision fusion is performed based on the local scale of the structure. The interest of the proposed method is verified using both visual and quantitative analyses for different Pléiades data sets.

Face Recognition using Sub-Holistic PCA

This paper proposes a face recognition scheme that enhances the correct face recognition rate as compared to conventional Principal Component Analysis (PCA). The proposed scheme, Sub-Holistic PCA (SH-PCA), was tested using ORL database and out performed PCA for all test scenarios. SH-PCA requires more computational power and memory as compared to PCA however it yields an improvement of 6% correct recognition on the complete ORL database of 400 images. The correct recognition rate for the complete ORL database is 90% for the SH-PCA technique.

A Multivariate Empirical Mode DecompositionBased Approach to Pansharpening

We propose a novel class of schemes for the pansharpening of multispectral (MS) images using a multivariate empirical mode decomposition (MEMD) algorithm. MEMD is an extension of the empirical mode decomposition (EMD) algorithm, which enables the decomposition of multivariate data into its intrinsic oscillatory scales. The ability of MEMD to process multichannel data directly by performing data-driven, local, and multiscale analysis makes it a perfect match for pansharpening applications, a task for which standard univariate EMD is ill-equipped due to the nonuniqueness, mode-mixing, and mode-misalignment issues. We show that MEMD overcomes the limitations of standard EMD and yields improved spatial and spectral performance in the context of pansharpening of MS images. The potential of the proposed schemes is further demonstrated through comparative analysis against a number of standard pansharpening algorithms on both simulated Pleiades and real-world IKONOS data sets.

Image fusion and spectral unmixing of hyperspectral images for spatial improvement of classification maps

In this paper we propose a new approach for the improvement of the spatial resolution of hyperspectral image classification maps combining both spectral unmixing and pansharpening approaches. The main idea is to use a spectral unmixing algorithm based on neural networks to retrieve the abundances of the endmembers present in the scene, and then use the spatial information retrieved from the pansharpened image to find the location of each endmember within the enhanced pixel according to the endmembers abundances. The proposed approach has been applied both to real and synthetic datasets.

Fusion of hyperspectral and panchromatic images: A hybrid use of indusion and nonlinear PCA

Generally, for optical satellite sensors spatial and spectral resolutions are highly correlated factors. In fact, given the design constraints of these sensors, there is an inverse relation between their spatial and spectral resolution. Thus, the hyperspectral sensors have a high spectral resolution i.e. large number of bands covering the electromagnetic spectrum, but a lower spatial resolution. On the other hand, panchromatic (PAN) images have the highest spatial resolution but no spectral diversity. For better utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by making use of a high spatial resolution PAN image in the context of pansharpening or image fusion. Several fusion approaches have been proposed in the literature. In this paper we propose the use of a hybrid algorithm combining substitution and injection methods. One of the main challenges in hyperspectral image fusion is the improvement of the spatial resolution, i.e. spatial details while preserving the original spectral information. This requires addition of pertinent spatial details to each band of the HS image. However, due to large number of bands the pansharpening of HS images is computationally expensive. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. In this paper we propose the use of non-linear principal components instead of the original HS bands as input to a fusion process to enhance the spatial resolution of the HS image.

Pansharpening of Hyperspectral images using spatial distortion optimization

This paper presents a novel method for the spatial quality improvement of low resolution Hyperspectral (HS) images by making use of a high resolution panchromatic (Pan) image. Since the introduction of all the details extracted from the Pan image into the upscaled HS images may result in spectral and spatial distortions, a detail injection model based on the optimization of QNR spatial quality index is proposed. This proposed model produces pansharpened images while preserving spectral fidelity. Also, to speed up the fusion process we propose to use the Universal Image Quality Index (UIQI) for dimensionality reduction before performing pansharpening. Finally, a comparison of the proposed method is presented with some existing pansharpening methods.