On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation

Mahrooghy, Majid; Younan, N. H.; Anantharaj, Valentine; Aanstoos, James V.; Yarahmadian, Shantia

doi:10.1109/jstars.2012.2201449

Cited by 19 publications

(8 citation statements)

References 32 publications

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“…Nowadays, many practical applications, such as optical remote sensing application [1], weather prediction [2], precipitation estimation [3] and deep space climate observatory mission [4], require accurate cloud observation techniques. However, cloud observation is currently performed by professional observers, which is traditionally labor-intensive and prone to producing observation errors.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

Liu

Zhang

et al. 2018

Remote Sensing

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The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task.

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Section: Introductionmentioning

confidence: 99%

Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

Liu

Zhang

et al. 2018

Remote Sensing

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“…Majid Mahrooghy et al [15] proposed a method to improve the cloud classification and precipitation estimation using Link-based Cluster Ensemble (LCE). Satellite Precipitation Estimation (SPE) which makes use of Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm is modified by incorporation of LCE which involves segmentation of infrared cloud images into patches; cloud patch feature extraction; clustering cloud patches using LCE; and dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using the images from GOES -12 satellite.…”

Section: Literature Surveymentioning

confidence: 99%

“…The analysis of clouds and their features is important for a wide variety of applications. It has been used for now casting to deliver accurate weather forecasts [14], rainfall, and satellite precipitation estimates [15], in the study of contrails [21], and various other day-to-day meteorological applications [7,16]. For cloud cover estimation, cloud detection plays a vital role, which classifies each pixel of all-sky images into clear sky element or cloud.…”

Section: Feature Extraction and Image Segmentationmentioning

confidence: 99%

Intelligent Methods Used for Obtaining Weather Derivatives: A Review

Rudrappa¹,

Vijapur²

2019

EAS

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Weather is the condition of the atmosphere and forecasting is predicting the condition of the atmosphere in near future. Weather forecasting is a formidable challenge as weather is a multi-dimensional, continuous and chaotic process. The distinct nature of the model forecasting in all situations accurately is challenging. Presently weather conditions are being obtained from satellites, Doppler radar, radio sounds, observations from aircraft and ground. Collected data is subjected to various statistical and machine learning techniques. These techniques can incorporate relatively simple observation of the sky to highly complex computerized mathematical models. Weather forecasting still remains a challenging issue, due to unpredictable and chaotic nature of weather. Even with present methods the weather forecasting system may still fail to predict weather attribute, therefore there is still scope left to improve these systems. The objective of carrying out the survey is to look forward on how machine learning can help us to improve weather parameter estimation. In this paper we report the different methods carried out by leading researchers, formidable challenges and present our views on development of efficient weather forecasting system. Also, we propose a method which makes use image processing and neural networks to achieve the weather parameter estimation.

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“…spectral features, textural features and contextual features can be used for classification of images (Haralick et al, 1973), which can also be applied for classification of clouds. We may achieve the cloud classification using satellite images or ground based images with the aid of image processing and ML methods (Haralick et al, 1973;Mahrooghy et al, 2012;Rudrappa & Vijapur, 2019). In, the present paper we propose the cloud classification using the ground based images by blending the Content Based Image Retrieval (CBIR) method along with k-means clustering method.…”

Section: Introductionmentioning

confidence: 99%

“…for detection of clouds super Pixel segmentation (sPs) method was used in (Liu et al, 2014). In (Mahrooghy et al, 2012) authors propose to use Link-based Cluster ensemble for the purpose of cloud classification and precipitation estimation. In (Liu et al, 2012) the authors propose a novel descriptor -Illumination-Invariant Completed Local Ternary Pattern (ICLTP); which tries to overcome the challenges posed in cloud classification using ground based images because of illumination presented in the images.…”

Section: Introductionmentioning

confidence: 99%

Cloud Classification Using Ground Based Images Using CBIR and K-Means Clustering

Rudrappa¹

2020

Biosci. Biotech. Res. Comm

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Artificial Intelligence (AI) and especially Machine learning (ML) is finding to be useful in many tasks that are simple to carryout to complex tasks that are found to be challenging in nature. One such application of ML is in classification of images. In this paper an attempt to blend the application of unsupervised ML (k-means clustering) approach along with content based image retrieval (CBIR) approach is presented to classify clouds. K-means is a simple approach which can be applied for image classification, also k-means easily adapts to new examples of classification. An attempt is made to combine the features of k-means and CBIR to classify the cloud images. It is performing a double check on the cloud image being classified. Clustering in included with CBIR to obtain an easy retrieval of cloud image. Three categories are chosen for classification-low level clouds, high level clouds and medium level clouds. The classification of clouds is achieved with the help of ground based images (or whole sky images). High resolution of ground based images can be obtained with the help of new high resolution cameras. These ground based images are processed to classify the clouds present in the images into the three categories as mentioned above. Ground based images captured by ground based cameras provide better ground truth. The results find its application in various domains such as agriculture, aviation, military, and various meteorological applications.

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On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation

Cited by 19 publications

References 32 publications

Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

Multimodal Ground-Based Cloud Classification Using Joint Fusion Convolutional Neural Network

Intelligent Methods Used for Obtaining Weather Derivatives: A Review

Cloud Classification Using Ground Based Images Using CBIR and K-Means Clustering

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