Missing Information Reconstruction of Remote Sensing Data: A Technical Review

Shen, Huanfeng; Li, Xinghua; Cheng, Qing; Zeng, Chao; Yang, Gang; Li, Huifang; Zhang, Liangpei

doi:10.1109/mgrs.2015.2441912

Cited by 397 publications

(205 citation statements)

References 90 publications

Supporting

Mentioning

201

Contrasting

Unclassified

Order By: Relevance

“…Taking into consideration poor atmospheric conditions, there is generally a great deal of missing information, which reduces usage rate and hinders the follow-up interpretation. Therefore, a number of missing information reconstruction techniques have been developed [67]. Utilizing multi-sensor fusion methods would be an interesting way to obtain the most representative fine scale long-term urban LST composites [68,69].…”

Section: The Representativeness Of Satellite Remote Sensing Data In Tmentioning

confidence: 99%

Impact of MODIS Quality Control on Temporally Aggregated Urban Surface Temperature and Long-Term Surface Urban Heat Island Intensity

Gawuć

Strużewska

2016

Remote Sensing

View full text Add to dashboard Cite

Surface Urban Heat Island (SUHI) is a phenomenon of high spatial and temporal variability. However, studies that investigate urban land surface temperature (LST) observed in different seasons frequently utilize a single satellite measurement and do not incorporate temporal composites. Temporally aggregated data increase clear sky coverage, which is important in many aspects of urban climatology. However, it is critical to account for possible errors and quality of the data that are utilized. The objective of this paper is to analyze the impact of MODIS Quality Control (QC) and the view angle on temporally aggregated urban surface temperature and long-term SUHI intensity. To achieve this, a weighted arithmetic mean was utilized whose weights were based on the view angle of satellite observation and the MODIS QC flags; namely, LST retrieval errors and emissivity errors. In order to investigate the impact of the MODIS QC on long-term LST composites, five exponential powers were applied to weights during the temporal aggregation process, resulting in five thresholds of best quality pixel promotion. It was found that there are significant differences between temporal composites that take into account the MODIS QC and the view angle and those that do not (obtained by means of a simple arithmetic mean with no weights applied), in terms of spatial distribution and density distribution of urban and rural LST. The differences were more distinctive in spring or daytime cases than in autumn or nighttime cases. The impact of the MODIS QC and the view angle on temporal composites was highest in the city center. Ten SUHI indicators were utilized. It was found that the impact on long-term SUHI intensity is weaker than on the spatial pattern of LST and that SUHI indicators are inconsistent.

show abstract

Section: The Representativeness Of Satellite Remote Sensing Data In Tmentioning

confidence: 99%

Impact of MODIS Quality Control on Temporally Aggregated Urban Surface Temperature and Long-Term Surface Urban Heat Island Intensity

Gawuć

Strużewska

2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…A good review for many of the recent work about image reconstruction in satellite images recovery is presented in Shen et al [9]. The different algorithms have been categorized according to the data relationship they exploit.…”

Section: Introductionmentioning

confidence: 99%

Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

Adam

Mönks

Esch

et al. 2018

The 2nd International Electronic Conference on Remote Sensing

View full text Add to dashboard Cite

Clouds and cloud-shadow are a persistent problem in all optical satellite imagery. Plenty of methods have been suggested in the literature to address this problem, and reconstruct the missing part of the optical signal. In this work, three methods representative of different approaches to the cloud removal problem are compared. The first method is temporal fitting using Fourier series, which benefits from the temporal continuity of the signal. The second method uses sparse spectral unmixing to fill in the missing areas. The third method employs radiometric consistency as a tool to determine the missing part of the signal. These three methods are first presented and their theoretical background described, followed by a discussion of their implied assumptions and general performance. A set of experiments using Landsat 8 time series with diverse land cover types were conducted. The quantitative results of the three methods using simulated clouds are presented. Finally, some concluding remarks about the relative advantages of the three approaches are listed, in addition to some recommendations about their use.

show abstract

“…In recent years, researchers have found sparse reconstruction techniques to outperform traditional methods based on based on temporal replacement or patch-based spatial replacement (Shen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Cloud Removal From Sentinel-2 Image Time Series Through Sparse Reconstruction From Random Samples

Cerra¹,

Bieniarz²,

Müller³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.

show abstract

Missing Information Reconstruction of Remote Sensing Data: A Technical Review

Cited by 397 publications

References 90 publications

Impact of MODIS Quality Control on Temporally Aggregated Urban Surface Temperature and Long-Term Surface Urban Heat Island Intensity

Impact of MODIS Quality Control on Temporally Aggregated Urban Surface Temperature and Long-Term Surface Urban Heat Island Intensity

Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

Cloud Removal From Sentinel-2 Image Time Series Through Sparse Reconstruction From Random Samples

Contact Info

Product

Resources

About