Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction

Wu, Teng; Hu, Xiangyun; Zhang, Yong; Zhang, Lulin; Tao, Pengjie; Lu, Luping

doi:10.1016/j.isprsjprs.2016.09.006

Cited by 28 publications

(20 citation statements)

References 30 publications

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“…Wu et al [21] proposed to merge stereo-matching results with external information coming from a digital elevation model acquired by the Shuttle Radar Topography Mission (SRTM). As shown by the authors, the wind speed cannot be neglected when working with high resolution images: the diffusion of the cloud layers during the acquisition can lead to bad estimations of the clouds altitudes.…”

Section: Related Workmentioning

confidence: 99%

Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers

Dagobert¹,

Gioi²,

Franchis³

et al. 2020

Image Processing on Line

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This paper proposes a cloud detection algorithm for Earth observation images obtained by pushbroom satellite imagers. The pushbroom technology induces an inter-band acquisition delay leading to a parallax effect for the clouds. We propose a method exploiting this characteristic thanks to the analysis of the inter-band disparity. Several other features discriminating clouds are also defined and all are merged to build a robust a contrario statistical decision. Experiments applied on scenes acquired by various pushbroom satellites such as Sentinel-2, RapidEye and WorldView-2 show the effectiveness of the proposed method. In particular, we demonstrate a balanced accuracy rate close to 98% for cloud and non cloud classification for Sentinel-2 images.

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Section: Related Workmentioning

confidence: 99%

Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers

Dagobert¹,

Gioi²,

Franchis³

et al. 2020

Image Processing on Line

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“…While ''YAOGAN-9'' is in its late stage of orbit, where moderate/little orbital maintenance is required, its data will be used for the detection of small-scale orbital maneuvers. The ''TIANHUI-1'' satellite launched from the Jiuquan Satellite Launch Centre, China, on 24 August 2010 has more maneuvers and less orbital control during the long-term control period and is used as the data source for the orbit detection of medium-scale orbital maneuvers described in this section [38]. The ''Envisat'' satellite was launched on Mar.…”

Section: Detection Of the Orbital Maneuver At Different Scalesmentioning

confidence: 99%

Mining Two-Line Element Data to Detect Orbital Maneuver for Satellite

et al. 2019

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“…The method or technique decided upon will likely present its own host of considerations as there are often drawbacks or trade-offs to appraise (Medjahed 2015). Imagery attributes relating to haze or cloud cover can make processes such as feature extraction more difficult (Bai et al 2016;Champion 2016;Dare 2005;Li et al 2017;Liu and Yamazaki 2012;Panem et al 2005;Ramesh and Satheesh Kumar 2013;Sun et al 2017;Wu et al 2016;Wu and Tang 2005). Terrain characteristics such as ridge lines, mining sites, river beds, gullies, rocks, or confounding scrubland aspects can mimic targeted features.…”

Section: Introductionmentioning

confidence: 99%

The impact of seasonality on multi-scale feature extraction techniques

Moore

McKee

2019

International Journal of Digital Earth

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A key component in constructing a broad-scale, gridded population dataset is fine resolution geospatial data accurately depicting the extent of human activity. Analogous datasets are often developed using a wide range of methods and classification techniques, including the use of spatial features, spectral features, or the coupling of both to identify the presence of man-made structures from high-resolution satellite imagery. By using spatial and textural-based descriptors to generate highresolution settlement layers for two dissimilar regions at the peak of seasonal disparity, this study attempts to quantify the influence of seasonality on the accuracy of a supervised, multi-scale, feature extraction framework for automated delineation of human settlement. Results generated by numerous models are evaluated against a reference dataset allowing for assessment of seasonal and feature differences in the context of accuracy. Global or regional mapping of human settlement requires the assemblage of high-resolution satellite images with variegated acquisition characteristics (season, sun elevation, off-nadir, etc.) to produce a cloud-free composite image from which features are extracted. Results of this study suggest an emphasis on imagery criteria, in particular acquisition date, could improve classification accuracy when mapping human settlement at scale.

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Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction

Cited by 28 publications

References 30 publications

Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers

Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers

Mining Two-Line Element Data to Detect Orbital Maneuver for Satellite

The impact of seasonality on multi-scale feature extraction techniques

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