Fusion of Multispectral and Panchromatic Images Based on Morphological Operators

Abstract

Nonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper, we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high-resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradient operators and demonstrate the suitability of this algorithm through the comparison with the state-of-the-art approaches. Four data sets acquired by the Pleiades, Worldview-2, Ikonos, and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor.

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

“…According to (10), the different approaches and methods belonging to this class are uniquely characterized by the low-pass filter employed for obtaining the image P L , by the presence or absence of a decimator/interpolator pair [43] and by the set of space-varying injection gains, either spatially uniform, {g k } k=1,...,N , or space-varying, {G k } k=1,...,N .…”

Enhanced Back-Projection as Postprocessing for Pansharpening

“…• GSA [11], which is an improved version of GS [10] to capture the spectral responses of sensors by optimizing the mean square error (MSE) with respect to the PAN image. • MF [20], which is based on the nonlinear decomposition scheme of morphological operators.…”

“…On the contrary, the MRA or spatial class usually achieves a better spectral quality in the pansharpening result than that of the CS class. The representative methods belonging to the MRA class are high-pass modulation (HPM) [17], which is also named smoothing filter-based intensity modulation (SFIM) [18], generalized Laplacian pyramid (GLP) [19], morphological-based fusion (MF) [20], among many others [21,22]. However, the MRA methods may make the fused results produce ring artifacts, leading to spatial distortion.…”

Multispectral Pansharpening Approach Using Pulse-Coupled Neural Network Segmentation

“…In addition, the proposed PSBP method is also compared with some state of art methods, i.e., ATWT method (Vivone et al, 2013) and morphological operators based fusion method (MOF) (Restaino et al, 2016). Figures 3 and 4, we can see that EXP method shows better spectral consistency but poor spatial properties.…”

“…The former class includes intensity-hue-saturation method (IHS) (Tu et al, 2001), principal component analysis method (PCA) (Psjr et al, 1991, Shah et al, 2008, and Gram-Schmidt (GS) method (Vivone et al, 2015). The MRA methods comprise decimated wavelet transform (DWT) (Mallat, 1989), undecimated wavelet transform (UDWT) (Nason et al, 1995), "à trous" wavelet transform (ATWT) (Vivone et al, 2013, Shensa, 1992, Laplacian pyramid (LP) (Burt et al, 2003), and morphological pyramids (Restaino et al, 2016). More specifically, the pansharpening methods aim to inject the spatial details of PAN image into the MS bands.…”