Texture and color based cloud detection

Baseski, Emre; Cenaras, Caglar

doi:10.1109/rast.2015.7208361

Cited by 17 publications

(7 citation statements)

References 17 publications

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“…In order to verify the effectiveness of the proposed method, we compared the proposed cloud detection architecture with SVM [12], neural network [11], and K-means [32] approaches.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Hughes et al [11] developed a neural network approach to detect clouds in Landsat images using spectral and spatial information. Başeski and Cenaras [12] trained an SVM classifier using texture characteristics to detect cloud in RGB images. These methods are manually designed features which rely on prior knowledge and have difficulty in accurately representing the cloud features in the complex environment [13].…”

Section: Introductionmentioning

confidence: 99%

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Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks

Yang

Fan

Bilal

et al. 2018

IJGI

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In high-resolution image data, multilevel cloud detection is a key task for remote sensing data processing. Generally, it is difficult to obtain high accuracy for multilevel cloud detection when using satellite imagery which only contains visible and near-infrared spectral bands. So, multilevel cloud detection for high-resolution remote sensing imagery is challenging. In this paper, a new multilevel cloud detection technique is proposed based on the multiple convolutional neural networks for high-resolution remote sensing imagery. In order to avoid input the entire image into the network for cloud detection, the adaptive simple linear iterative clustering (A-SCLI) algorithm was applied to the segmentation of the satellite image to obtain good-quality superpixels. After that, a new multiple convolutional neural networks (MCNNs) architecture is designed to extract multiscale features from each superpixel, and the superpixels are marked as thin cloud, thick cloud, cloud shadow, and non-cloud. The results suggest that the proposed method can detect multilevel clouds and obtain a high accuracy for high-resolution remote sensing imagery.

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“…In order to verify the effectiveness of the proposed method, we compared the proposed cloud detection architecture with SVM [12], neural network [11], and K-means [32] approaches.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks

Yang

Fan

Bilal

et al. 2018

IJGI

View full text Add to dashboard Cite

show abstract

“…Thus, when working with VHR imagery from these sources, we only have a true-color RGB image. To handle these images, numerous approaches (Başeski and Cenaras 2015;Bai et al 2016;Fan et al 2017) of different complexity have been developed, which have an overall accuracy close to that when applying one of the algorithms that uses thermal bands.…”

Section: Problems Associated With the Quality Of Vhr Imagerymentioning

confidence: 99%

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

et al. 2019

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The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change.

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“…Chenthan [30] has compared Linear, Polynomial, and Gaussian-RBF kernel in SVMs and concluded the combination of texture and Linear SVM with correct classification rate of 92.30% in cloud detection. Başeski [31] used color and line based elimination as well as texture based SVM classification to detect cloud on RGB images. Latry considered SVM cloud detection within PLEIADES-HR framework [32].…”

Section: Introductionmentioning

confidence: 99%

Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion

et al. 2016

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Abstract:The accurate location of clouds in images is prerequisite for many high-resolution satellite imagery applications such as atmospheric correction, land cover classifications, and target recognition. Thus, we propose a novel approach for cloud detection using machine learning and multi-feature fusion based on a comparative analysis of typical spectral, textural, and other feature differences between clouds and backgrounds. To validate this method, we tested it on 102 Gao Fen-1(GF-1) and Gao Fen-2(GF-2) satellite images. The overall accuracy of our multi-feature fusion method for cloud detection was more than 91.45%, and the Kappa coefficient for all the tested images was greater than 80%. The producer and user accuracy were also higher at 93.67% and 95.67%, respectively; both of these values were higher than the values for the other tested feature fusion methods. Our results show that this novel multi-feature approach yields better accuracy than other feature fusion methods. In post-processing, we applied an object-oriented method to remove the influence of highly reflective ground objects and further improved the accuracy. Compared to traditional methods, our new method for cloud detection is accurate, exhibits good scalability, and produces consistent results when mapping clouds of different types and sizes over various land surfaces that contain natural vegetation, agriculture land, built-up areas, and water bodies.

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Texture and color based cloud detection

Cited by 17 publications

References 17 publications

Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks

Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion

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