A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kučera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

doi:10.1016/j.isprsjprs.2011.03.005

Cited by 42 publications

(20 citation statements)

References 20 publications

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“…In that sense, they perfectly fit the two assumptions used in the region-growing step: this explains why our procedure is so successful. By contrast, in a similar way as the other methods found in literature (Sedano et al, 2011), the detection of mists appears to be more difficult. This is due to the fact that these clouds are generally thinner and less bright than e.g.…”

Section: Resultsmentioning

confidence: 53%

“…By contrast, only a few of them make a full use of multi-temporal satellite images. In this category, the method by Sedano et al (2011), originally designed for single-date High-Resolution images, uses information from multi-temporal Low-Resolution MODIS images in order to perform the cloud detection. Thus, the difference between MODIS and input images is analysed to extract the seed points that correspond to clouds, which are then used in a subsequent region-growing procedure in order to delineate clouds finely.…”

Section: Related Workmentioning

confidence: 99%

“…Following the design of the method by Sedano et al (2011), our method is composed of two steps: seeds are firstly extracted and secondly extended during a region-growing procedure in order to delineate clouds finely. However, our method differs from this latter method in the way to extract seeds.…”

Section: Presentationmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Cloud Detection From Multi-Temporal Satellite Images: Towards the Use of Pléiades Time Series

Champion¹

2012

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

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ABSTRACT:Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time series that generally contain between 5 and 10 images) and is based on a region-growing procedure. Seeds (corresponding to clouds) are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here assumed to be related to a high variation of reflectance between two images). Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at the end of 2011). In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery.

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Section: Resultsmentioning

confidence: 53%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Cloud Detection From Multi-Temporal Satellite Images: Towards the Use of Pléiades Time Series

Champion¹

2012

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

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“…However, VHSR sensors do not come with such, and, when available in medium resolution it is of a coarser resolution than the other channels (e.g., the spatial resolution of Landsat 7 ETM+ is 60 m while that of Landsat 8 TIRS is 100 m [48]). Some alternatives proposed to address this limitation have been the use of Markov random fields [49], linear spectral unmixing [50], pixel-based seed identification and object-based region growing [51], and a multi-temporal approach at constant viewing angles [35]. Some cloud-specific masking algorithms are the AFAR algorithm (ACOLITE/FMASK Aquatic Refined) developed by the Royal Belgian Institute of Natural Sciences (RBINS) , the Automatic Cloud Cover Assessment modified (ACCAm) algorithm (ACCA modified) by VITO, and Idepix developed by Brockmann Consult GmbH.…”

Section: Cloud Maskingmentioning

confidence: 99%

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Stratoulias

Tolpekin

et al. 2017

Remote Sensing

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Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

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“…The method used in this paper to detect clouds is very similar to the one described in (Champion, 2012) and appears to be a compilation of the ideas found in (Hagolle et al, 2010), (Sedano et al, 2011) and(Le Hégarat-Mascle andAndré, 2009 From there, a pixel-to-pixel analysis is carried out between the Reference Ortho-image RO and each PO. If the radiometric difference is better than a given threshold T R , the pixel of the Reference Ortho-image receives a positive vote.…”

Section: Cloud Detectionmentioning

confidence: 99%

Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

Champion¹

2016

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

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ABSTRACT:Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences

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A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

Cited by 42 publications

References 20 publications

Automatic Cloud Detection From Multi-Temporal Satellite Images: Towards the Use of Pléiades Time Series

Automatic Cloud Detection From Multi-Temporal Satellite Images: Towards the Use of Pléiades Time Series

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

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