Bi-cubic interpolation for shift-free pan-sharpening

Most of pan-sharpening techniques require the re-sampling of the multi-spectral (MS) image for matching the size of the panchromatic (Pan) image, before the geometric details of Pan are injected into the MS image. This operation is usually performed in a separable fashion by means of symmetric digital low-pass filtering kernels with odd lengths that utilize piecewise local polynomials, typically implementing linear or cubic interpolation functions. Conversely, constant, i.e. nearest-neighbour, and quadratic kernels, implementing zero and two degree polynomials, respectively, introduce shifts in the magnified images, that are sub-pixel in the case of interpolation by an even factor, as it is the most usual case. However, in standard satellite systems, the point spread functions (PSF) of the MS and Pan instruments are centered in the middle of each pixel. Hence, commercial MS and Pan data products, whose scale ratio is an even number, are relatively shifted by an odd number of half pixels. Filters of even lengths may be exploited to compensate the half-pixel shifts between the MS and Pan sampling grids. In this paper, it is shown that separable polynomial interpolations of odd degrees are feasible with linear-phase kernels of even lengths. The major benefit is that bi-cubic interpolation, which is known to represent the best trade-off between performances and computational complexity, can be applied to commercial MS + Pan datasets, without the need of performing a further half-pixel registration after interpolation, to align the expanded MS with the Pan image.     

Using pan-sharpened high resolution satellite data to improve impervious surfaces estimation

Impervious surface is an important environmental and socio-economic indicator for numerous urban studies. While a large number of researches have been conducted to estimate the area and distribution of impervious surface from satellite data, the accuracy for impervious surface estimation (ISE) is insufficient due to high diversity of urban land cover types. This study evaluated the use of panchromatic (PAN) data in very high resolution satellite image for improving the accuracy of ISE by various pan-sharpening approaches, with a further comprehensive analysis of its scale effects. Three benchmark pan-sharpening approaches, Gram-Schmidt (GS), PANSHARP and principal component analysis (PCA) were applied to WorldView-2 in three spots of Hong Kong. The on-screen digitization were carried out based on Google Map and the results were viewed as referenced impervious surfaces. The referenced impervious surfaces and the ISE results were then re-scaled to various spatial resolutions to obtain the percentage of impervious surfaces. The correlation coefficient (CC) and root mean square error (RMSE) were adopted as the quantitative indicator to assess the accuracy. The accuracy differences between three research areas were further illustrated by the average local variance (ALV) which was used for landscape pattern analysis. The experimental results suggested that 1) three research regions have various landscape patterns; 2) ISE accuracy extracted from pan-sharpened data was better than ISE from original multispectral (MS) data; and 3) this improvement has a noticeable scale effects with various resolutions. The improvement was reduced slightly as the resolution became coarser.     

A new pan-sharpening method using multiobjective particle swarm optimization and the shiftable contourlet transform

In this paper, a novel approach based on multiobjective particle swarm optimization (MOPSO) is presented for panchromatic (Pan) sharpening of a multispectral (MS) image. This new method could transfer spatial details of the pan image into a high-resolution version of the MS image, while color information from the low-resolution MS image is well preserved. The pan and MS images are locally different because of different resolutions, and therefore we cannot directly combine them in the spatial domain. For this reason, we generate two initial results, which are more appropriate for a weighted combination. First, the pan and the MS images are histogram matched. Then we use the shiftable contourlet transform (SCT) to decompose the histogram-matched pan and MS images. The SCT is a new shiftable and modified version of the contourlet transform. In this step, an algorithm based on the SCT is used to generate two initial results of the high-resolution MS images. Our objective is to produce two modified high-resolution MS images, in which one has high spatial similarity to the pan image and the other one has high radiometric quality in each band. Therefore, we have used two different fusion rules to integrate the high-frequency contourlet coefficients of the pan and MS images to generate two initial results of high-resolution MS image or the pan-sharpened (PS) image. Finally, we can find the optimal PS image by applying the MOPSO algorithm and using the two initial PS results. Specifically, the PS image is obtained via a weighted combination of the two initial results, in which the weights are locally estimated via a multiobjective particle swarm optimization algorithm to generate a PS image with high spatial and radiometric qualities. Based on experimental results obtained, the produced pan-sharpened image also has good spectral quality. The efficiency of the proposed method is tested by performing pan-sharpening of high-resolution (Quickbird and Wordview2) and medium-resolution (Landsat-7 ETM +) datasets. Extensive comparisons with the state-of-the-art pan-sharpening algorithms indicate that our new method provides improved subjective and objective results.     

遥感影像融合AIHS转换与粒子群优化算法

Pan-sharpening是通过将低分辨率多光谱图像(LMS)与高分辨率全色图像(PAN)进行合成而获得高光谱高空间分辨率的多光谱图像(HMS)的过程。本文提出一种Pan-sharpening方法,称为PAIHS。该方法基于自适应亮度-色度-饱和度(AIHS)转换和变分Pan-sharpening框架以及两个假设(①Pan-sharpening图像和原始多光谱图像(MS)具有相同的光谱信息;②Pan-sharpening图像与全色图像(PAN)包含的几何信息保持一致),同时确定目标函数,然后用粒子群算法(PSO)进行优化,目的是得到最佳控制参数并求得目标函数最小值,此时对应着最好的Pan-sharpening质量。试验结果表明,本文提出的方法具有高效性和可靠性,获得的性能指标也优于目前一些主流的融合方法。