Remote Sensing Image Mosaicking: Achievements and Challenges

Li, Xinghua; Feng, Ruitao; Guan, Xiaobin; Shen, Huanfeng; Zhang, Liangpei

doi:10.1109/mgrs.2019.2921780

Cited by 110 publications

(56 citation statements)

References 120 publications

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“…The empirical linear correction model is widely used for radiometric normalization [4] [9] [10] [9], which is shown as = * + , (7) where represents the digital number of the corresponding pixel of satellite images, which is the radiation observation of the radiometric block adjustment in this paper, does not represent the corresponding observation on the adjacent image or the real measured radiation as other references, but refers to the VRCP in our method, and a and b are the intercept and slope of the linear model, respectively.…”

Section: Mathematical Model Of Radiometric Adjustmentmentioning

confidence: 99%

“…However, existing methods can only solve one or two problems. For example, the path based pairwise techniques suffer from the error accumulation [6] [7], and the statistics based global optimization algorithms cannot eliminate outliers well [8] [9]. Thus, the paper presents a comprehensive relative radiometric normalization method for HRSI via radiometric block adjustment without selecting master images to solve three problems simultaneously.…”

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confidence: 99%

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Robust Radiometric Normalization of Multitemporal Satellite Images Via Block Adjustment Without Master Images

Liu

Tang

Tao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Determining appropriate master images, reducing radiometric error accumulation, and eliminating outliers from the cloud, water, and land changes, are three main issues in radiometric normalization of multitemporal high resolution satellite images (HRSI) during mosaicking. However, these three issues have not been simultaneously considered by the existing methods. This paper presents a comprehensive radiometric normalization method for multitemporal HRSI using a radiometric block adjustment without master images. Pseudo-invariant features (PIFs) extracted from image pairs using the iteratively reweighted multivariate alteration detection are used as the corresponding pixel observations and organized to form radiometric tie points according to the corresponding horizontal space coordinates. Radiometric error equations are subsequently constructed, and the linear radiometric transformation parameters are solved by a global adjustment. The time-invariant PIFs generally represent the true corresponding features and naturally avoid the cloud, water, and land changes, which can eliminate effects of outliers. Furthermore, the pixel values of tie points calculated from the weighted average of the corresponding pixel observations are used as virtual radiometric control points to eliminate the dependency on master images. Moreover, a global optimum can be achieved by the global adjustment, effectively overcoming the error accumulation, which is severe in large datasets. Four groups of HRSI datasets from various satellites are used to validate the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms two state-of-the-art methods and has good applicability and stability, considering both visual effects and quantitative performance. Index Terms-radiometric normalization; multitemporal satellite images; multivariate alteration detection; global radiometric block adjustment I. INTRODUCTION ADIOMETRIC normalization aims at reducing the radiation differences between images by adjusting the color of each image [1]. Satellite images which form a block are often acquired at different time or even by different satellites, so their colors are different. Radiometric normalization is essential when using satellite images to produce some products which require seamless geometric and radiometric mosaicking. Radiometric normalizations can be Manuscript received July, 2020.

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Section: Mathematical Model Of Radiometric Adjustmentmentioning

confidence: 99%

mentioning

confidence: 99%

Robust Radiometric Normalization of Multitemporal Satellite Images Via Block Adjustment Without Master Images

Liu

Tang

Tao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The pre-processing included mosaicking the six tiles, re-projecting them into Universal Transverse Mercator (UTM) 45N, and extracting the TP. Thereafter, 6141 MOD10A1 and 5644 MYD10A1 images during 2001-2017 were acquired, as a few images with incorrect mosaics were discarded [43].…”

Section: Datamentioning

confidence: 99%

A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau

Jing

Shen

et al. 2019

Remote Sensing

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The Tibetan Plateau (TP) is an important component of the global environmental system, on which the snow cover greatly affects the regional climate and ecology. Moderate resolution imaging spectroradiometer (MODIS) snow cover products have been demonstrated to be appropriate for investigating the snow cover over the TP. However, they are subject to cloud obscuration, and the TP’s extremely complex terrain makes the snow monitoring difficult. Therefore, in this paper, we propose a two-stage spatio–temporal fusion framework for the cloud removal of MODIS C6 snow products, including an adjusted Terra and Aqua combination (TAC) and a spatio–temporal fusion based on Gaussian kernel function and error correction (STF-GKF-EC). To the best of our knowledge, this is the first time that a spatio–temporally continuous daily 500-m MODIS normalized difference snow index (NDSI) product has been generated for the TP, which greatly improves the spatial and temporal resolutions of the current snow cover products. The main stage, STF-GKF-EC, adaptively weights the spatial and temporal correlations by the Gaussian kernel function, and further takes the rapid changes of snow cover into consideration through the error correction. The experiments indicated that STF-GKF-EC removes clouds completely, achieving an overall accuracy (OA) and mean absolute error (MAE) of 91.48% and 3.88, respectively. Based on the cloud-removed results, during 2001–2017, as far as the intra-annual variation is concerned, a large proportion of the snow cover appears between October and May, with a peak in February/March, and the variation is mainly controlled by temperature. For the inter-annual variation, an obvious increasing trend of 0.68/year for NDSI is observed before 2005, followed by a slight decreasing trend of 0.16/year, in which precipitation is a better explanation factor than temperature.

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“…The main urban areas often cannot be covered in only one image due to the limitations of the imaging swath width or imaging mechanism. Stitching or mosaicking multiple images from different periods or sensors is often a necessary order to capture the full target area [14,[53][54][55]. Image registration [56] is usually required before the mosaicking of multiple images.…”

Section: Related Workmentioning

confidence: 99%

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

Feng

et al. 2019

Remote Sensing

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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.

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Remote Sensing Image Mosaicking: Achievements and Challenges

Cited by 110 publications

References 120 publications

Robust Radiometric Normalization of Multitemporal Satellite Images Via Block Adjustment Without Master Images

Robust Radiometric Normalization of Multitemporal Satellite Images Via Block Adjustment Without Master Images

A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau

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

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