Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images

Taravat, Alireza; Frate, Fabio Del; Cornaro, Cristina; Vergari, Stefania

doi:10.1109/lgrs.2014.2356616

Cited by 122 publications

(70 citation statements)

References 15 publications

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“…This motivated the design of a new cloud mask using machine-learning techniques based on ANN algorithms. Unlike the ANN recently proposed by Taravat et al [55], that only provides daytime cloud detection, our platform includes season-specific ANNs:…”

Section: Thermal Anomaly Detectionmentioning

confidence: 99%

North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)

Gonzalez

Briottet

2017

Remote Sensing

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Abstract:The Meteosat Second Generation (MSG) geostationary platform equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument provides observations of the Earth every 15 min since 2004. Based on those measurements, we present a new method called North African Sandstorm Survey (NASCube) to: (i) generate day/night remote sensing images in order to detect sandstorms over the Sahara and Saudi Arabia; and (ii) estimate day and night aerosol optical depth (AOD). This paper presents a method to create true color day and night images from the SEVIRI instrument level 1.5 products and the complete operational data processing system to detect sandstorms and quantify the AOD over the desert areas of North Africa and Saudi Arabia. The designed retrieval algorithms are essentially based on the use of artificial neural networks (ANN), which seems to be well suited to this issue. Our methods are validated against two different datasets, namely the Deep Blue NASA moderate-resolution imaging spectroradiometer (MODIS) product and AErosol RObotic NETwork (AERONET) acquisitions located in desert areas. It is shown that NASCube products deliver better estimations for high AOD (>0.2) over land areas than Deep Blue products. The open-public web platform will help researchers to identify, quantify and retrieve the impact of sandstorms over desert regions.

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Section: Thermal Anomaly Detectionmentioning

confidence: 99%

North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)

Gonzalez

Briottet

2017

Remote Sensing

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“…The logistic sigmoid function ranges from 0 to +1. However, it is sometimes desirable to have the activation function range from −1 to +1, in which case the activation function assumes an anti-symmetric form (e.g., tanh function) with respect to the origin [89,91].…”

Section: An Overview Of Multilayer Perceptron Neural Network (Mlp Nns)mentioning

confidence: 99%

A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes

Taravat

Rajaei²,

Emadodin

et al. 2016

Water

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Lake Urmia, the second largest saline Lake on earth and a highly endangered ecosystem, is on the brink of a serious environmental disaster similar to the catastrophic death of the Aral Sea. Progressive drying has been observed during the last decade, causing dramatic changes to Lake Urmia's surface and its regional water supplies. The present study aims to improve monitoring of spatiotemporal changes of Lake Urmia in the period 1975-2015 using the multi-temporal satellite altimetry and Landsat (5-TM, 7-ETM+ and 8-OLI) images. In order to demonstrate the impacts of climate change and human pressure on the variations in surface extent and water level, Lake Sevan and Van Lake with different characteristics were studied along with the Urmia Lake. Normalized Difference Water Index-Principal Components Index (NDWI-PCs), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), and MultiLayer Perceptron Neural Networks (MLP NNs) classifier were investigated for the extraction of surface water from Landsat data. The presented results revealed that MLP NNs has a better performance in the cases where the other models generate poor accuracy. The results show that the area of Lake Sevan and Van Lake have increased while the area of Lake Urmia has decreased by~65.23% in the past decades, far more than previously reported (~25% to 50%). Urmia Lake's shoreline has been receding severely between 2010 and 2015 with no sign of recovery, which has been partly blamed on prolonged droughts, aggressive regional water resources development plans, intensive agricultural activities, and anthropogenic changes to the system. The results also indicated that (among the proposed factors) changes in inflows due to overuse of surface water resources and constructing dams (mostly during 1995-2005) are the main reasons for Urmia Lake's shoreline receding. The model presented in this manuscript can be used by managers as a decision support system to find the effects of building new dams or other infrastructures.

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“…With the development of machine learning technology, machine-learning-based cloud detection methods [33,34] have attracted increasing study, which can extract more robust and high-level information from images. Hu et al [35] adopted a visual attention technique in computer vision based on random forest to automatically identify images with a significant cloud cover.…”

Section: Introductionmentioning

confidence: 99%

A Cloud Detection Method for Landsat 8 Images Based on PCANet

Xie

Jiang

2018

Remote Sensing

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Cloud detection for remote sensing images is often a necessary process, because cloud is widespread in optical remote sensing images and causes a lot of difficulty to many remote sensing activities, such as land cover monitoring, environmental monitoring and target recognizing. In this paper, a novel cloud detection method is proposed for multispectral remote sensing images from Landsat 8. Firstly, the color composite image of Bands 6, 3 and 2 is divided into superpixel sub-regions through Simple Linear Iterative Cluster (SLIC) method. Then, a two-step superpixel classification strategy is used to predict each superpixel as cloud or non-cloud. Thirdly, a fully connected Conditional Random Field (CRF) model is used to refine the cloud detection result, and accurate cloud borders are obtained. In the two-step superpixel classification strategy, the bright and thick cloud superpixels, as well as the obvious non-cloud superpixels, are firstly separated from potential cloud superpixels through a threshold function, which greatly speeds up the detection. The designed double-branch PCA Network (PCANet) architecture can extract the high-level information of cloud, then combined with a Support Vector Machine (SVM) classifier, the potential superpixels are correctly classified. Visual and quantitative comparison experiments are conducted on the Landsat 8 Cloud Cover Assessment (L8 CCA) dataset; the results indicate that our proposed method can accurately detect clouds under different conditions, which is more effective and robust than the compared state-of-the-art methods.

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Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images

Cited by 122 publications

References 15 publications

North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)

North Africa and Saudi Arabia Day/Night Sandstorm Survey (NASCube)

A Spaceborne Multisensory, Multitemporal Approach to Monitor Water Level and Storage Variations of Lakes

A Cloud Detection Method for Landsat 8 Images Based on PCANet

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