Abstract Pansharpening is an efficient way of producing images of higher spectral and spatial fidelity. Since pansharpening aims to generate a spectrally enhanced image with the same spatial detail content of the source panchromatic (PAN) image, the source multispectral (MS) image is upsampled to the size of the source PAN image prior to pansharpening. Several image interpolation algorithms have been proposed for this purpose, which may lead the analysts to a confusion as to which of these algorithms should be used for the best pansharpening performance. Hence, this study aimed to investigate the role of widely used image interpolation algorithms in the quality of the pansharpened images. For this purpose, the nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, interpolation with a polynomial kernel of 23 coefficients (INTERP23) and cubic spline interpolation algorithms were tested through several pansharpening techniques on three test sites with different characteristics. Investigations revealed that upsampling the source MS images with the INTERP23 algorithm resulted in the pansharpened images with the optimum spectral and spatial quality.

Summary Pansharpening aims to fuse a lower‐resolution multispectral (MS) image and a higher‐resolution panchromatic image, resulting in an image with the color quality of the former and spatial detail quality of the latter. Of all, the component substitution (CS)‐based pansharpening methods have drawn attentions with their ability to produce sharp images. Despite their success in sharpening the images, these methods deteriorate the color features of the input MS images due of the uncertainty in the calculation of the intensity component used by them. Previous studies showed that attempts to preserve the color features tend to cause spatial detail loss to a certain extent. This, of course, reveals the necessity of a compromise between the spectral and spatial fidelity of the pansharpened images produced by the CS‐based techniques. This study proposed using the multi‐objective Non‐Dominated Sorting Genetic Algorithm‐II metaheuristic algorithm with the CS‐based methods to optimize the intensity component to find the best compromise between the spectral and spatial fidelity of the pansharpened images. The proposed framework was applied on two commonly used pansharpening techniques, Gram‐Schmidt and Synthetic Variable Ratio. It was found that the proposed methods managed to find the best balance between the color and spatial fidelity.

Abstract The majority of the ground filtering techniques proposed so far use several user‐defined parameter values. Since no standard protocols exist to define these parameters, obtaining the optimum filtering performance is very hard, especially in large‐extent areas with abrupt topography changes. This, of course, reveals the necessity of some more efficient strategies to ease the ground filtering process in such areas. Utilizing classified images for ground filtering purpose may be of help to achieve this. Hence, this study, for the first time in the literature, investigated the performances of the state‐of‐the‐art machine learning algorithms maximum likelihood (ML), artificial neural network (ANN), support vector machines (SVM), and random forest (RF) in ground filtering of a UAS‐based point cloud. The used approaches were based on the assignment of the points corresponding to the ground‐related classes to the ground class. Evaluations showed that the SVM‐based ground filtering approach achieved the optimum filtering result. The SVM‐, ML‐, RF‐, and ANN‐based ground filtering methods achieved the total errors of 13.2%, 16.4%, 19.6%, and 21.9% in the test site, respectively.