Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Abstract

Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

A Large-Scale Mapping Scheme for Urban Building From Gaofen-2 Images Using Deep Learning and Hierarchical Approach

“…The rapid development of cities requires research on all aspects of urbanization. Existing large-scale urban mapping research includes urban building type mapping [4]- [6], urban functional zone mapping [7], [8], local climate zone mapping [9], urban building height mapping [10], [11], urban region mapping [12], [13], and more. Urban building mapping includes many small-scale studies but few large-scale ones.…”

Escalating the resolution of an urban aerial image via novel shadow amputation algorithm

“…In recent years, a number of techniques for identifying shadows in high-resolution, remotely sensed pictures have been presented. It can be split into two sub-groups: (i) Geometric approaches technique, which requires prior information about scene location, elevation, and alignment (Kang et al 2017;Wang et al 2017) and (ii) Property-based algorithm, which does not require explicit prior knowledge and rely on specific shadow features, such as spatial and spectral characteristics (Li et al 2020;Sun et al 2020). This technique can be further split into the threshold method approach and the machine learning-based approach (Zhang et al 2019;Reddy and Nagaraju 2017;Dare 2005].…”

Monitoring The IIUM River Using Unmanned Aerial Vehicle And Image Classification

“…Its applications include satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement [12][13][14]. In this study, alternative approaches toward the image processing methods; stitching or mosaicking [15,16], georeferencing [17], and supervised classification [18], will be used.…”