Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

Download Full-text

A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI

Atmospheric Measurement Techniques ◽

10.5194/amt-6-2713-2013 ◽

2013 ◽

Vol 6 (10) ◽

pp. 2713-2723 ◽

Cited By ~ 16

Author(s):

S. Bley ◽

H. Deneke

Keyword(s):

High Resolution ◽

Small Scale ◽

Cloud Detection ◽

Low Resolution ◽

Data Set ◽

Clear Sky ◽

Infrared Imager ◽

Cloud Optical Thickness ◽

Cloud Mask ◽

Spectral Channels

Abstract. A threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the Meteosat SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low-resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures that cannot be detected by the low-resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behavior for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test data set depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as an estimate of cloud fraction. The HRV cloud mask aims for small-scale convective sub-pixel clouds that are missed by the EUMETSAT cloud mask. The major limit of the HRV cloud mask is the minimum cloud optical thickness (COT) that can be detected. This threshold COT was found to be about 0.8 over ocean and 2 over land and is highly related to the albedo of the underlying surface.

Download Full-text

A Novel Approach for Cloud Detection in Scenes with Snow/Ice Using High Resolution Sentinel-2 Images

Atmosphere ◽

10.3390/atmos10020044 ◽

2019 ◽

Vol 10 (2) ◽

pp. 44 ◽

Cited By ~ 1

Author(s):

Ling Han ◽

Tingting Wu ◽

Qing Liu ◽

Zhiheng Liu

Keyword(s):

High Resolution ◽

Satellite Images ◽

Matched Filter ◽

Spectral Characteristics ◽

Cloud Detection ◽

Novel Approach ◽

Visible Wavelength Range ◽

Number Frequency ◽

Detection Effect ◽

Sentinel 2

The recognition of snow versus clouds causes difficulties in cloud detection because of the similarity between cloud and snow spectral characteristics in the visible wavelength range. This paper presents a novel approach to distinguish clouds from snow to improve the accuracy of cloud detection and allow an efficient use of satellite images. Firstly, we selected thick and thin clouds from high resolution Sentinel-2 images and applied a matched filter. Secondly, the fractal digital number-frequency (DN-N) algorithm was applied to detect clouds associated with anomalies. Thirdly, spatial analyses, particularly spatial overlaying and hotspot analyses, were conducted to eliminate false anomalies. The results indicate that the method is effective for detecting clouds with various cloud covers over different areas. The resulting cloud detection effect possesses specific advantages compared to classic methods, especially for satellite images of snow and brightly colored ground objects with spectral characteristics similar to those of clouds.

Download Full-text

Operating Characteristics for Indicator Or-ing of Incoherently Combined Matched-Filter Outputs

10.21236/ada188485 ◽

1987 ◽

Cited By ~ 2

Author(s):

Albert H. Nuttall

Keyword(s):

Matched Filter ◽

Operating Characteristics

Download Full-text

Ultra-High-Resolution 1 m/pixel CaSSIS DTM Using Super-Resolution Restoration and Shape-from-Shading: Demonstration over Oxia Planum on Mars

Remote Sensing ◽

10.3390/rs13112185 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2185

Author(s):

Yu Tao ◽

Sylvain Douté ◽

Jan-Peter Muller ◽

Susan J. Conway ◽

Nicolas Thomas ◽

...

Keyword(s):

High Resolution ◽

3D Reconstruction ◽

Imaging System ◽

Processing System ◽

Digital Terrain Model ◽

Super Resolution ◽

Landing Site ◽

Shape From Shading ◽

Stereo Pair ◽

Terrain Model

We introduce a novel ultra-high-resolution Digital Terrain Model (DTM) processing system using a combination of photogrammetric 3D reconstruction, image co-registration, image super-resolution restoration, shape-from-shading DTM refinement, and 3D co-alignment methods. Technical details of the method are described, and results are demonstrated using a 4 m/pixel Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) panchromatic image and an overlapping 6 m/pixel Mars Reconnaissance Orbiter Context Camera (CTX) stereo pair to produce a 1 m/pixel CaSSIS Super-Resolution Restoration (SRR) DTM for different areas over Oxia Planum on Mars—the future ESA ExoMars 2022 Rosalind Franklin rover’s landing site. Quantitative assessments are made using profile measurements and the counting of resolvable craters, in comparison with the publicly available 1 m/pixel High-Resolution Imaging Experiment (HiRISE) DTM. These assessments demonstrate that the final resultant 1 m/pixel CaSSIS DTM from the proposed processing system has achieved comparable and sometimes more detailed 3D reconstruction compared to the overlapping HiRISE DTM.

Download Full-text

A Ground Moving Target Detection Approach Based on Shadow Feature With Multichannel High-Resolution Synthetic Aperture Radar

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2016.2597159 ◽

2016 ◽

Vol 13 (10) ◽

pp. 1572-1576 ◽

Cited By ~ 10

Author(s):

Huajian Xu ◽

Zhiwei Yang ◽

Guozhong Chen ◽

Guisheng Liao ◽

Min Tian

Keyword(s):

High Resolution ◽

Synthetic Aperture Radar ◽

Target Detection ◽

Synthetic Aperture ◽

Moving Target ◽

Moving Target Detection ◽

Detection Approach ◽

Ground Moving Target Detection ◽

Aperture Radar

Download Full-text

High resolution mapping of methane emissions from marine and terrestrial sources using a Cluster-Tuned Matched Filter technique and imaging spectrometry

Remote Sensing of Environment ◽

10.1016/j.rse.2013.03.018 ◽

2013 ◽

Vol 134 ◽

pp. 305-318 ◽

Cited By ~ 35

Author(s):

Andrew K. Thorpe ◽

Dar A. Roberts ◽

Eliza S. Bradley ◽

Christopher C. Funk ◽

Philip E. Dennison ◽

...

Keyword(s):

High Resolution ◽

Matched Filter ◽

Methane Emissions ◽

Imaging Spectrometry ◽

Filter Technique ◽

High Resolution Mapping ◽

Resolution Mapping

Download Full-text

AN INTEGRATED PHOTOGRAMMETRIC AND PHOTOCLINOMETRIC APPROACH FOR PIXEL-RESOLUTION 3D MODELLING OF LUNAR SURFACE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1117-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1117-1122

Author(s):

W. C. Liu ◽

B. Wu

Keyword(s):

High Resolution ◽

Lunar Surface ◽

Image Matching ◽

Point Cloud ◽

3D Modelling ◽

Conjugate Points ◽

Stereo Pair ◽

Pixel Resolution ◽

Dense Image ◽

Lunar Reconnaissance Orbiter

High-resolution 3D modelling of lunar surface is important for lunar scientific research and exploration missions. Photogrammetry is known for 3D mapping and modelling from a pair of stereo images based on dense image matching. However dense matching may fail in poorly textured areas and in situations when the image pair has large illumination differences. As a result, the actual achievable spatial resolution of the 3D model from photogrammetry is limited by the performance of dense image matching. On the other hand, photoclinometry (i.e., shape from shading) is characterised by its ability to recover pixel-wise surface shapes based on image intensity and imaging conditions such as illumination and viewing directions. More robust shape reconstruction through photoclinometry can be achieved by incorporating images acquired under different illumination conditions (i.e., photometric stereo). Introducing photoclinometry into photogrammetric processing can therefore effectively increase the achievable resolution of the mapping result while maintaining its overall accuracy. This research presents an integrated photogrammetric and photoclinometric approach for pixel-resolution 3D modelling of the lunar surface. First, photoclinometry is interacted with stereo image matching to create robust and spatially well distributed dense conjugate points. Then, based on the 3D point cloud derived from photogrammetric processing of the dense conjugate points, photoclinometry is further introduced to derive the 3D positions of the unmatched points and to refine the final point cloud. The approach is able to produce one 3D point for each image pixel within the overlapping area of the stereo pair so that to obtain pixel-resolution 3D models. Experiments using the Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC NAC) images show the superior performances of the approach compared with traditional photogrammetric technique. The results and findings from this research contribute to optimal exploitation of image information for high-resolution 3D modelling of the lunar surface, which is of significance for the advancement of lunar and planetary mapping.

Download Full-text

A fused road detection approach in high resolution multi-spectrum remote sensing imagery

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. ◽

10.1109/igarss.2005.1526290 ◽

2005 ◽

Cited By ~ 1

Author(s):

Yan Dongmei ◽

Zhao Zhongming ◽

Chen Zhong

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Road Detection ◽

Remote Sensing Imagery ◽

Detection Approach

Download Full-text

RESERVOIR SCALE CHEMOSTRATIGRAPHY AND FACIES MODELING USING HIGH SAMPLE RATE GEOPHYSICAL SCANS OF WHOLE CORE

10.30632/spwla-2021-0051 ◽

2021 ◽

Author(s):

John J. Degenhardt ◽

◽

Safdar Ali ◽

Mansoor Ali ◽

Brian Chin ◽

...

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

High Resolution ◽

Operating Characteristics ◽

Eagle Ford Shale ◽

Eagle Ford ◽

Vertical Heterogeneity ◽

Austin Chalk ◽

Sample Rate ◽

Petrophysical Logs

Many unconventional reservoirs exhibit a high level of vertical heterogeneity in terms of petrophysical and geo-mechanical properties. These properties often change on the scale of centimeters across rock types or bedding, and thus cannot be accurately measured by low-resolution petrophysical logs. Nonetheless, the distribution of these properties within a flow unit can significantly impact targeting, stimulation and production. In unconventional resource plays such as the Austin Chalk and Eagle Ford shale in south Texas, ash layers are the primary source of vertical heterogeneity throughout the reservoir. The ash layers tend to vary considerably in distribution, thickness and composition, but generally have the potential to significantly impact the economic recovery of hydrocarbons by closure of hydraulic fracture conduits via viscous creep and pinch-off. The identification and characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other pertinent geologic lithofacies. This paper involves high-resolution laboratory measurements of geophysical properties over whole core and analysis of such data using machine-learning techniques to build novel high-resolution facies models that can be used to make statistically meaningful predictions of facies characteristics in proximally remote wells where core or other physical is not available. Multiple core wells in the Austin Chalk/Eagle Ford shale play in Dimmitt County, Texas, USA were evaluated. Drill core was scanned at high sample rates (1 mm to 1 inch) using specialized equipment to acquire continuous high resolution petrophysical logs and the general modeling workflow involved pre-processing of high frequency sample rate data and classification training using feature selection and hyperparameter estimation. Evaluation of the resulting training classifiers using Receiver Operating Characteristics (ROC) determined that the blind test ROC result for ash layers was lower than those of the better constrained carbonate and high organic mudstone/wackestone data sets. From this it can be concluded that additional consideration must be given to the set of variables that govern the petrophysical and mechanical properties of ash layers prior to developing it as a classifier. Variability among ash layers is controlled by geologic factors that essentially change their compositional makeup, and consequently, their fundamental rock properties. As such, some proportion of them are likely to be misidentified as high clay mudstone/wackestone classifiers. Further refinement of such ash layer compositional variables is expected to improve ROC results for ash layers significantly.

Download Full-text