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 Ship Detection in Optical Satellite Images via Directional Bounding Boxes Based on Ship Center and Orientation Prediction

2019 ◽  
Vol 11 (18) ◽  
pp. 2173 ◽  
Author(s):  
Jinlei Ma ◽  
Zhiqiang Zhou ◽  
Bo Wang ◽  
Hua Zong ◽  
Fei Wu
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Ship Detection ◽  
Bounding Boxes ◽  
Typical Object ◽  
Optical Satellite Images

To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, and then we can predict rotated bounding boxes from rotated region proposals to locate arbitrary-oriented ships more accurately. The two networks share the same deconvolutional layers to perform semantic segmentation for the prediction of center regions and orientations of ships, respectively. They can provide the potential center points of the ships helping to determine the more confident locations of the region proposals, as well as the ship orientation information, which is beneficial to the more reliable predetermination of rotated region proposals. Classification and regression are then performed for the final ship localization. Compared with other typical object detection methods for natural images and ship-detection methods, our method can more accurately detect multiple ships in the high-resolution remote sensing image, irrespective of the ship orientations and a situation in which the ships are docked very closely. Experiments have demonstrated the promising improvement of ship-detection performance.

Fast detection of moving objects based on sequential images processing

2020 ◽  
Vol 39 (4) ◽  
pp. 5037-5044
Author(s):  
Peng-Cheng Wei ◽  
Fangcheng He ◽  
Jing Li
Keyword(s):  
Moving Objects ◽  
Feature Matching ◽  
Detection Method ◽  
Detection Methods ◽  
Dynamic Scenes ◽  
Practical Applications ◽  
Fast Detection ◽  
Wide Range ◽  
Images Processing ◽  
Sequential Images

Nowadays, moving object detection in sequence images has become a hot topic in computer vision research, and has a very wide range of practical applications in many fields of military and daily life. In this paper, fast detection of moving objects in complex background is studied, and fast detection methods for moving objects in static and dynamic scenes are proposed respectively. Firstly, based on image preprocessing, aiming at the difficulty of feature extraction of moving targets in low illumination at night, Gamma change is used to process. Secondly, for the fast detection of moving objects in static scenes, this paper designs a detection method combining background difference and edge frame difference. Finally, aiming at the fast detection of moving objects in dynamic scenes, a feature matching detection method based on the SIFT algorithm is designed in this paper. Simulation experiments show that the method designed in this paper has good detection performance.

Characterization of Cirrus Clouds in the Mid-Latitude Tropopause Region

2021 ◽  
Author(s):  
Irene Bartolome Garcia ◽  
Reinhold Spang ◽  
Jörn Ungermann ◽  
Sabine Griessbach ◽  
Michael Höpfner ◽  
...  
Keyword(s):  
Detection Method ◽  
Cirrus Clouds ◽  
Radiation Budget ◽  
Climate Projections ◽  
Cloud Detection ◽  
Reanalysis Dataset ◽  
Height Determination ◽  
Tropopause Region ◽  
Research Aircraft

<p>Cirrus clouds contribute to the general radiation budget of the Earth, playing an important role in climate projections. Of special interest are optically thin cirrus clouds close to the tropopause due to the fact that they are difficult to capture and thus their impact is not yet well understood. This study presents a characterization of cirrus clouds observed by the limb sounder GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) aboard the German research aircraft HALO during the WISE (Wave-driven ISentropic Exchange) campaign in September/October 2017. This campaign took place in Shannon, Ireland (52.70°N, 8.86°W).  We developed an optimized cloud detection method and derived macro-physical characteristics of the detected cirrus clouds: cloud top height, cloud bottom height, vertical extent and cloud top position with respect to the tropopause. The fraction of cirrus clouds detected above the tropopause (> 0 km) is in the order of 13% to 27%, depending on the detection method and the definition of the tropopause. In general, good agreement with the clouds predicted by the ERA5 reanalysis dataset is obtained. However, cloud occurrence is ≈50% higher in the observations for the region close to and above the tropopause. Cloud bottom heights are also detected above the tropopause. Considering the uncertainties for the tropopause height, cloud top height and cloud bottom height determination we could not find unambiguous evidence for the formation of cirrus layers above the tropopause. In addition, for a better understanding of the tropopause cirrus properties and life conditions, two cirrus cases observed during two scientific flights were selected from  the observations and compared with cirrus simulations performed with the 3D Lagrangian microphysical model  CLaMS-Ice, which is based on the two-moment bulk  cirrus model by Spichtinger and Gierens (2009) (doi: 10.5194/acp-9-685-2009). The model is fed by backward trajectories computed from high resolution ERA5 data (hourly, spatial grid 30 km). This contribution summarizes and extends on work described by Bartolome Garcia et al. (2020) (doi:10.5194/amt-2020-394).</p>

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 Observation of Cirrus Clouds with GLORIA during the WISE Campaign: Detection Methods and Cirrus Characterization

2020 ◽  
Author(s):  
Irene Bartolome Garcia ◽  
Reinhold Spang ◽  
Jörn Ungermann ◽  
Sabine Griessbach ◽  
Martina Krämer ◽  
...  
Keyword(s):  
Reanalysis Data ◽  
Cirrus Clouds ◽  
Detection Sensitivity ◽  
Radiation Budget ◽  
Detection Methods ◽  
Climate Projections ◽  
Cloud Detection ◽  
Data Set ◽  
Research Aircraft

Abstract. Cirrus clouds contribute to the general radiation budget of the Earth, playing an important role in climate projections. Of special interest are optically thin cirrus clouds close to the tropopause due to the fact that their impact is not yet well understood. Measuring these clouds is challenging as both high spatial resolution as well as a very high detection sensitivity are needed. These criteria are fulfilled by the infrared limb sounder GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere). This study presents a characterization of observed cirrus clouds using the data obtained by GLORIA aboard the German research aircraft HALO during the WISE (Wave-driven ISentropic Exchange) campaign in September/October 2017. We developed an optimized cloud detection method and derived macro-physical characteristics of the detected cirrus clouds such as cloud top height, cloud top bottom height, vertical extent and cloud top position with respect to the tropopause. The fraction of cirrus clouds detected above the tropopause is in the order of 13 % to 27 %. In general, good agreement with the clouds predicted by the ERA5 reanalysis data-set is obtained. However, cloud occurrence is ≈ 50 % higher in the observations for the region close to and above the tropopause. Cloud bottom heights are also detected above the tropopause. However, considering the uncertainties, we cannot confirm the formation of unattached cirrus layers above the tropopause.

scholarly journals A Method for Wheat Head Detection Based on Yolov4

2020 ◽  
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection Method ◽  
Computer Hardware ◽  
Detection Methods ◽  
Plant Phenotyping ◽  
Wide Range ◽  
Head Detection ◽  
Multi Level

Abstract Background: Plant phenotyping by deep learning has increased attention. The detection of wheat head in the field is an important mission for estimating the characteristics of wheat heads such as the density, health, maturity, and presence or absence of awns. Traditional wheat head detection methods have problems such as low efficiency, strong subjectivity, and poor accuracy. However, with the development of deep learning theory and the iteration of computer hardware, the accuracy of object detection method using deep neural networks has been greatly improved. Therefore, using a deep neural network method to detect wheat heads in images has a certain value. Results: In this paper, a method of wheat head detection based on deep neural network is proposed. Firstly, for improving the backbone network part, two SPP networks are introduced to enhance the ability of feature learning and increase the receptive field of the convolutional network. Secondly, the top-down and bottom-up feature fusion strategies are applied to obtain multi-level features. Finally, we use Yolov3's head structures to predict the bounding box of object. The results show that our proposed detection method for wheat head has higher accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed of our proposed method is 88fps. Conclusion: The proposed deep neural network can accurately and quickly detector the wheat head in the image which is based on Yolov4. In addition, the training dataset is a wheat head dataset with accurate annotations and rich varieties, which makes the proposed method more robust and has a wide range of application values. The proposed detector is also more suitable for wheat detection task, with the deeper backbone networks. The use of spatial pyramid pooling (SPP) and multi-level features fusion, which all play a crucial role in improving detector performance. Our method provides beneficial help for the breeding of wheat

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.

scholarly journals HIERARCHICAL CLUSTERING ALGORITHM FOR DETECTING ANOMALOUS PROFILES IN COMPUTER SYSTEMS

2014 ◽  
pp. 72-78
Author(s):  
Rachid Beghdad
Keyword(s):  
Intrusion Detection ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Detection Methods ◽  
System A ◽  
Unix System ◽  
Mining Methods ◽  
Simulation Results ◽  
Hierarchical Clustering Algorithm

We introduce a new intrusion detection method based on the Hierarchical Clustering Algorithm (HCA), to detect anomalous user’s profiles. In the Unix system, a simple user has only some privileges (can access to some resources), but the root user has more privileges. So, we can speak here about hierarchy of users. By the same way, we can use a hierarchy of users in intrusion detection field, to distinguish between the normal user and suspicious user. Many data mining methods were already used in previous works in intrusion detection. Even if some of them led to interesting results, but they still suffer from some weaknesses. This is the reason why we focused in this study on the use of the HCA to detect anomalous profiles. A survey of intrusion detection methods is presented. The HCA procedure is described in detail. Our simulation results demonstrate the robustness of our approach in comparison to some previous used methods.

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