Automatic Cloud Detection Using Spectral Rationing and Fuzzy Clustering

Surya, S R; Simon, Philomina

doi:10.1109/adcons.2013.44

Cited by 18 publications

(9 citation statements)

References 8 publications

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“…This method is time-consuming as both the algorithms are iterative optimization algorithms. Authors of [15] used spectral image rationing technique for generating ratio image based on the color transformation of the input image. Further fuzzy C-means clusters the ratio image and detects the clouds automatically.…”

Section: Cloud/no Cloudmentioning

confidence: 99%

Cloud detection methodologies: variants and development—a review

Mahajan

Fataniya

2019

Complex Intell. Syst.

132

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Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004-2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/ No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement.

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Section: Cloud/no Cloudmentioning

confidence: 99%

Cloud detection methodologies: variants and development—a review

Mahajan

Fataniya

2019

Complex Intell. Syst.

132

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“…Such a classification is not a simple task (Li, Zang, Zhang, Li, & Wu, 2014;Mountrakis, Im, & Ogole, 2011). Classification of clouds is considered important and useful for many real-time applications namely weather forecast, surface identification, and climate-change detection (Elhag & Bahrawi, 2014;Surya & Simon, 2013). Surya and Simon (2013) have designed a clouddetection algorithm, which would perform color transformation on any given input image to generate a ratio image using spectral-image-rationing technique, and finally clusters the ratio image using Fuzzy C-means clustering for detecting clouds automatically.…”

Section: Literature Surveymentioning

confidence: 99%

Fuzzy statistics-based affinity propagation technique for clustering in satellite cloud image

Devika

Parthasarathy²

2018

European Journal of Remote Sensing

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Satellite-remote-sensing technologies have set off improvements in atmospheric research and developments of new tools in prospect of discovery to monitor. Classification of cloud image through satellite is well recognized as a valid approach in many climatic and environmental analyses. A multispectral cloud classifier was implemented to automate the interpretation of Kalpana-1 satellite image. In this paper, a novel image-clustering method, grounded on fuzzy statistics-based affinity propagation (FS-AP), has been proposed. It entails two steps: feature extraction and clustering. The objective is to study the volatility of the FS-AP for the classification of satellite cloud images optimally. Methods for classifying cloud type from satellite images are difficult in terms of efficiency and accuracy. Results show the effectiveness of the proposed technique for classification of satellite cloud image by comparing with fuzzy K-means and affinity propagation.

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“…Laban N, Nasr A et al (2012) developed the multi-scale cloud extraction of remote-sensing images using spatial and texture features [6]. Surya S. R and Simon P (2013) used color space transform and Fuzzy C-means clustering to extract cloud in Landsat images [7]. Goodwin N. R, Collett L. J et al (2013) proposed a fast cloud detection algorithm for the Landsat images using the hierarchical processing and combining the spectral information of the multi-temporal images [8].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Accurate High-Resolution Satellite Image Retrieval Method

Fan

Zhang

et al. 2017

Remote Sensing

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With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.

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Automatic Cloud Detection Using Spectral Rationing and Fuzzy Clustering

Cited by 18 publications

References 8 publications

Cloud detection methodologies: variants and development—a review

Cloud detection methodologies: variants and development—a review

Fuzzy statistics-based affinity propagation technique for clustering in satellite cloud image

An Automatic Accurate High-Resolution Satellite Image Retrieval Method

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