No abstract

In remote sensing, owing to existing sensors’ limitations and the tradeoff between signal-to-noise ratio (SNR) and instantaneous field of view (IFOV), it is difficult to obtain a single image with good spectral and spatial resolution. Pansharpening (PS) is the technique for sharpening multispectral (MS) images by extracting structural and edge information of panchromatic (PAN) image. Multiscale decomposition methods are used for decomposing image in sub-bands but are affected by ringing artifacts, therefore the resultant image seems to be blurred and misregistered. The proposed method overcomes this drawback by decomposing PAN and four band MS image into cartoon and texture components with total variation (TV) Hilbert[Formula: see text] model. The particle swarm optimization (PSO) algorithm is used for finding the optimum weight for fusing texture and cartoon details of PAN and MS images. The proposed method is practically validated on both full-scale and reduced-scale. Robustness of our proposed approach is tested on different geographical areas such as hilly, urban, and vegetation areas. From the visual analysis and qualitative parameters, the proposed method is proved effective compared with other traditional approaches.

Inspite of technological advancement, inherent processing capability of current age sensors limits the desired details in the acquired image for variety of remote sensing applications. Pan-sharpening is a prominent scheme to integrate the essential spatial details inferred from panchromatic (PAN) image and the desired spectral information of multispectral (MS) image. This paper presents an effective two-stage pan-sharpening method to produce high resolution multispectral (HRMS) image. The proposed method is based on the premise that the HRMS image can be formulated as an amalgam of spectral and spatial components. The spectral components are estimated by processing the interpolated MS image with a filter approximated with modulation transfer function (MTF) of the sensor. Sparse representation theory is adapted to construct the spatial components. The high-frequency details extracted from the PAN image and its low resolution variant are utilized to construct dual dictionaries. The dictionaries are jointly learned by an efficient training algorithm to enhance the adaptability. The hypothesis of sparse coefficients invariance over scales is also incorporated to reckon the appropriate spatial information. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four distinct datasets generated from QuickBird, IKONOS, Pléiades and WorldView-2 sensors are used for experimentation. The comprehensive assessment at reduced-scale and full-scale persuade the effectiveness of proposed method in the retention of spectral information and intensification of the spatial details.

In remote sensing due to several constraints of optical imaging sensors high spectral and high spatial features are not present in single image, so high frequency component of panchromatic(PAN) image needs to be restored at the spatial resolution of corresponding multispectral (MS) image. Misbalancing between spatial and spectral details extraction and injection leads to several issues like spectral distortion, intensity variation. Direct fusion methods often generate resultant image with poor contrast and several undesirable effects, while multi resolution analysis (MRA) techniques have achieved success in many fusion algorithms, these methods also suffer from ringing artifact due to strong edge decomposition. In this paper, pansharpening (PS) method is based on total variation-Hilbert − 1(TV-H− 1) model for decomposing MS and PAN image in appropriate cartoon and texture components. To refine the detail map and better quality of fused image adaptive weights are calculated using particle swarm optimization (PSO). For experimental analysis, four different sensors datasets with different regions like city area, hilly area and vegetation area are used. Resultant image and image quality analysis parameters confirm that the proposed method succeed in spectral detail preservation compared with other widely used pansharpening methods.

The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image.