scholarly journals Cloud detection for MIPAS using singular vector decomposition

2009 ◽  
Vol 2 (2) ◽  
pp. 1185-1219
Author(s):  
J. Hurley ◽  
A. Dudhia ◽  
R. G. Grainger
Keyword(s):  
Climate Models ◽  
Singular Vector ◽  
Detection Method ◽  
Michelson Interferometer ◽  
Detection Methods ◽  
Cloud Detection ◽  
Radiative Balance ◽  
The Past ◽  
Vector Decomposition ◽  
Atmospheric Sounding

Abstract. Clouds are increasingly recognised for their influence on the radiative balance of the Earth and the implications that they have on possible climate change, as well as in air pollution and acid-rain production. However, clouds remain a major source of uncertainty in climate models. Satellite-borne high-resolution limb sounders, such as the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT, provide information on clouds, especially optically thin clouds, which have been difficult to observe in the past. The aim of this work is to develop, implement and test a reliable cloud detection method for infrared spectra measured by MIPAS. Current MIPAS cloud detection methods used operationally have been developed to detect thick cloud filling more than 30% of the measurement field-of-view (FOV). In order to resolve thin clouds, a new detection method using Singular Vector Decomposition (SVD) is formulated and tested. A rigorous comparison of the current operational and newly-developed detection methods for MIPAS is carried out – and the new SVD detection method has been proven to be much more reliable than the current operational method, and very sensitive even to thin clouds only marginally filling the MIPAS FOV.

scholarly journals Cloud detection for MIPAS using singular vector decomposition

2009 ◽  
Vol 2 (2) ◽  
pp. 533-547 ◽  
Author(s):  
J. Hurley ◽  
A. Dudhia ◽  
R. G. Grainger
Keyword(s):  
Singular Vector ◽  
Detection Method ◽  
Michelson Interferometer ◽  
High Spectral Resolution ◽  
Detection Methods ◽  
Cloud Detection ◽  
The Past ◽  
Vector Decomposition ◽  
Atmospheric Sounding ◽  
Thin Cloud

Abstract. Satellite-borne high-spectral-resolution limb sounders, such as the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT, provide information on clouds, especially optically thin clouds, which have been difficult to observe in the past. The aim of this work is to develop, implement and test a reliable cloud detection method for infrared spectra measured by MIPAS. Current MIPAS cloud detection methods used operationally have been developed to detect cloud effective filling more than 30% of the measurement field-of-view (FOV), under geometric and optical considerations – and hence are limited to detecting fairly thick cloud, or large physical extents of thin cloud. In order to resolve thin clouds, a new detection method using Singular Vector Decomposition (SVD) is formulated and tested. This new SVD detection method has been applied to a year's worth of MIPAS data, and qualitatively appears to be more sensitive to thin cloud than the current operational method.

scholarly journals Influences of the Indian Summer Monsoon on Water Vapor and Ozone Concentrations in the UTLS as Simulated by Chemistry–Climate Models

2010 ◽  
Vol 23 (13) ◽  
pp. 3525-3544 ◽  
Author(s):  
Markus Kunze ◽  
Peter Braesicke ◽  
Ulrike Langematz ◽  
Gabriele Stiller ◽  
Slimane Bekki ◽  
...  
Keyword(s):  
Water Vapor ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Large Scale ◽  
Climate Models ◽  
Lower Stratosphere ◽  
Activity Index ◽  
Michelson Interferometer ◽  
Upper Troposphere ◽  
Atmospheric Sounding

Abstract The representation of the Indian summer monsoon (ISM) circulation in some current chemistry–climate models (CCMs) is assessed. The main assessment focuses on the anticyclone that forms in the upper troposphere and lower stratosphere and the related changes in water vapor and ozone during July and August for the recent past. The synoptic structures are described and CCMs and reanalysis models are compared. Multiannual means and weak versus strong monsoon cases as classified by the Monsoon–Hadley index (MHI) are discussed. The authors find that current CCMs capture the average synoptic structure of the ISM anticyclone well as compared to the 40-yr ECMWF Re-Analysis (ERA-40) and NCEP–NCAR reanalyses. The associated impact on water vapor and ozone in the upper troposphere and lower stratosphere as observed with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat is captured by most models to some degree. The similarities for the strong versus weak monsoon cases are limited, and even for present-day conditions the models do not agree well for extreme events. Nevertheless, some features are present in the reanalyses and more than one CCM, for example, ozone increases at 380 K eastward of the ISM. With the database available for this study, future changes of the ISM are hard to assess. The modeled monsoon activity index used here shows slight weakening of the ISM circulation in a future climate, and some of the modeled water vapor increase seems to be contained in the anticyclone at 360 K and sometimes above. The authors conclude that current CCMs capture the average large-scale synoptic structure of the ISM well during July and August, but large differences for the interannual variability make assessments of likely future changes of the ISM highly uncertain.

scholarly journals An Unsupervised Approach for Detecting Group Shilling Attacks in Recommender Systems Based on Topological Potential and Group Behaviour Features

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hongyun Cai ◽  
Fuzhi Zhang
Keyword(s):  
Recommender Systems ◽  
Detection Method ◽  
Potential Method ◽  
Detection Methods ◽  
Clustering Method ◽  
Group Behaviour ◽  
The Past ◽  
Group Members ◽  
Unsupervised Approach ◽  
Individual Features

To protect recommender systems against shilling attacks, a variety of detection methods have been proposed over the past decade. However, these methods focus mainly on individual features and rarely consider the lockstep behaviours among attack users, which suffer from low precision in detecting group shilling attacks. In this work, we propose a three-stage detection method based on strong lockstep behaviours among group members and group behaviour features for detecting group shilling attacks. First, we construct a weighted user relationship graph by combining direct and indirect collusive degrees between users. Second, we find all dense subgraphs in the user relationship graph to generate a set of suspicious groups by introducing a topological potential method. Finally, we use a clustering method to detect shilling groups by extracting group behaviour features. Extensive experiments on the Netflix and sampled Amazon review datasets show that the proposed approach is effective for detecting group shilling attacks in recommender systems, and the F1-measure on two datasets can reach over 99 percent and 76 percent, respectively.

scholarly journals SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images

2021 ◽  
Vol 13 (15) ◽  
pp. 2910
Author(s):  
Xiaolong Li ◽  
Hong Zheng ◽  
Chuanzhao Han ◽  
Wentao Zheng ◽  
Hao Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Method ◽  
Detection Methods ◽  
Utilization Rate ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Deep Convolutional Neural Networks

Clouds constitute a major obstacle to the application of optical remote-sensing images as they destroy the continuity of the ground information in the images and reduce their utilization rate. Therefore, cloud detection has become an important preprocessing step for optical remote-sensing image applications. Due to the fact that the features of clouds in current cloud-detection methods are mostly manually interpreted and the information in remote-sensing images is complex, the accuracy and generalization of current cloud-detection methods are unsatisfactory. As cloud detection aims to extract cloud regions from the background, it can be regarded as a semantic segmentation problem. A cloud-detection method based on deep convolutional neural networks (DCNN)—that is, a spatial folding–unfolding remote-sensing network (SFRS-Net)—is introduced in the paper, and the reason for the inaccuracy of DCNN during cloud region segmentation and the concept of space folding/unfolding is presented. The backbone network of the proposed method adopts an encoder–decoder structure, in which the pooling operation in the encoder is replaced by a folding operation, and the upsampling operation in the decoder is replaced by an unfolding operation. As a result, the accuracy of cloud detection is improved, while the generalization is guaranteed. In the experiment, the multispectral data of the GaoFen-1 (GF-1) satellite is collected to form a dataset, and the overall accuracy (OA) of this method reaches 96.98%, which is a satisfactory result. This study aims to develop a method that is suitable for cloud detection and can complement other cloud-detection methods, providing a reference for researchers interested in cloud detection of remote-sensing images.

Reconciling modelled and observed age of air through SF6 sinks

2020 ◽  
Author(s):  
Sheena Loeffel ◽  
Roland Eichinger ◽  
Hella Garny ◽  
Thomas Reddmann ◽  
Stefan Versick ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Climate Models ◽  
Climate Model ◽  
Michelson Interferometer ◽  
Positive Trend ◽  
Physical Processes ◽  
Overturning Circulation ◽  
Atmospheric Sounding ◽  
Sensitivity Studies

<p>Mean age of air (AoA) is a common diagnostic for the stratospheric overturning circulation in both climate models and observations. Observations of AoA mostly base on measurements of SF6, which is an almost ideal AoA tracer because its emssions across the recent decades increased nearly linearly and it is fairly stable in the troposphere and stratosphere. Over the last ten years, however, researchers were puzzled as to why AoA climatologies and trends of model simulations and observational data do not coincide. AoA in climate models is generally much lower than in observations and models show a clear decrease of AoA over time while measurements show a non-significant increase.</p><p>What is commonly not considered in the models is that SF6 has chemical sinks in the mesosphere, and these lead to apparently older air in the stratosphere. In our experiment, we explicitely calculate SF6 sinks based on physical processes in simulations with the global chemistry-climate model EMAC (ECHAM MESSy Atmospheric Chemistry). We show that considering the SF6 removal reactions strongly increases stratospheric AoA and leads to much better agreement between the climatologies of EMAC and MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) satellite observations. Moreover, the stratospheric AoA trend over the recent decades reverses sign when we derive it from SF6 with sinks. This means that the trend can such be reconciled with the trend that has been derived from long-term balloon-borne measurements. Our specifically designed sensitivity studies moreover reveal that this positive trend results neither from circulation changes, nor from variations of the reactive species involved in mesospheric SF6 depletion. Instead, it is generated through the temporally growing influence of the SF6 sinks themselves, an effect that overcompensates the negative trend resulting from the accelerating stratospheric overturning circulation.</p>

scholarly journals Retrieval of macrophysical cloud parameters from MIPAS: algorithm description and preliminary validation

2010 ◽  
Vol 3 (4) ◽  
pp. 3877-3906
Author(s):  
J. Hurley ◽  
A. Dudhia ◽  
R. G. Grainger
Keyword(s):  
Atmospheric Chemistry ◽  
A Priori ◽  
Michelson Interferometer ◽  
Optimal Estimation ◽  
Additional Information ◽  
The Past ◽  
Cloud Parameters ◽  
Preliminary Validation ◽  
Atmospheric Sounding ◽  
Cloud Top Temperature

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT has the potential to be particularly useful for studying high, thin clouds, which have been difficult to observe in the past. This paper details the development, implementation and testing of an optimal-estimation-type retrieval for three macrophysical cloud parameters (cloud top height, cloud top temperature and cloud extinction coefficient) from infrared spectra measured by MIPAS, employing additional information derived to improve the choice of a priori. The retrieval is applied and initially validated on MIPAS data. From application to MIPAS data, the retrieved cloud top heights are assessed to be accurate to within 50 m, the cloud top temperatures to within 0.5 K and extinction coefficients to within a factor of 15%. This algorithm has been adopted by the European Space Agency's ''MIPclouds'' project, which itself recognises the potential of MIPAS beyond monitoring atmospheric chemistry and seeks to study clouds themselves rigorously using MIPAS.

scholarly journals CNN Feature-Based Image Copy Detection with Contextual Hash Embedding

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1172
Author(s):  
Zhili Zhou ◽  
Meimin Wang ◽  
Yi Cao ◽  
Yuecheng Su
Keyword(s):  
Detection Method ◽  
Superior Performance ◽  
Detection Methods ◽  
Copy Detection ◽  
Visual Words ◽  
The Past ◽  
Index File ◽  
Image Copy Detection ◽  
Feature Based ◽  
Deep Learning Features

As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applications of computer vision. However, it is not feasible to directly apply the existing global CNN features for copy detection, since they are usually sensitive to partial content-discarded attacks, such as copping and occlusion. Thus, we propose a local CNN feature-based image copy detection method with contextual hash embedding. We first extract the local CNN features from images and then quantize them to visual words to construct an index file. Then, as the BOW quantization process decreases the discriminability of these features to some extent, a contextual hash sequence is captured from a relatively large region surrounding each CNN feature and then is embedded into the index file to improve the feature’s discriminability. Extensive experimental results demonstrate that the proposed method achieves a superior performance compared to the related works in the copy detection task.

scholarly journals An Effective Method for Detecting Clouds in GaoFen-4 Images of Coastal Zones

2020 ◽  
Vol 12 (18) ◽  
pp. 3003
Author(s):  
Zheng Wang ◽  
Jun Du ◽  
Junshi Xia ◽  
Cheng Chen ◽  
Qun Zeng ◽  
...  
Keyword(s):  
Satellite Images ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Radiation Budget ◽  
Detection Methods ◽  
Coastal Zones ◽  
Cloud Detection ◽  
Free Region ◽  
Wide Range ◽  
Cloudy Region

Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds that exert a significant impact on the radiation budget of planet earth. Although the traditional cloud detection methods have generally performed well, these methods were usually developed specifically for particular sensors in a particular region with a particular underlying surface (e.g., land, water, vegetation, and man-made objects). Coastal regions are known to have a variety of underlying surfaces, which represent a major challenge in cloud detection. Therefore, there is an urgent requirement for developing a cloud detection method that could be applied to a variety of sensors, situations, and underlying surfaces. In the present study, a cloud detection method based on spatial and spectral uniformity of clouds was developed. In addition to having a spatially uniform texture, a spectrally approximate value was also present between the blue and green bands of the cloud region. The blue and green channel data appeared more uniform over the cloudy region, i.e., the entropy of the cloudy region was lower than that of the cloud-free region. On the basis of this difference in entropy, it would be possible to categorize the satellite images into cloud region images and cloud-free region images. Furthermore, the performance of the proposed method was validated by applying it to the data from various sensors across the coastal zone of the South China Sea. The experimental results demonstrated that compared to the existing operational algorithms, EN-clustering exhibited higher accuracy and scalability, and also performed robustly regardless of the spatial resolution of the different satellite images. It is concluded that the EN-clustering algorithm proposed in the present study is applicable to different sensors, different underlying surfaces, and different regions, with the support of NDSI and NDBI indices to remove the interference information from snow, ice, and man-made objects.

scholarly journals A SIMPLE CLOUD DETECTION METHOD FOR GF-1 WFV IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1197-1201
Author(s):  
L. L. Jia ◽  
X. Q. Wang
Keyword(s):  
Detection Method ◽  
Texture Feature ◽  
New Method ◽  
Detection Methods ◽  
Cloud Detection ◽  
Segmentation Method ◽  
Optical Images ◽  
Routine Work ◽  
Cloud Mask ◽  
Better Than

Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature (BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90 %. The proposed method can meet the needs of routine work.

Sign in / Sign up

Export Citation Format

Share Document