Cloud detection in all sky ConCam images by Gaussian fitting and valley detection in histogram
Authors:
- 1 August 2015
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 275-278
Abstract
Clouds are the complex part to classify in the all sky Continuous Camera (ConCam) image due to their intensity similar to other objects and their diverse nature. The paper presents a histogram based technique to extract clouds in ConCam images. Small scale features are extracted by subtracting Gaussian fitted histogram from original histogram of an image. Valleys are then detected in these small scale features and based on these valleys, i.e. their intensity levels, different regions of sky are classified.Keywords
This publication has 9 references indexed in Scilit:
- Cloud detection based on segmentation with statistical and geometry features2012
- Thin Cloud Detection of All-Sky Images Using Markov Random FieldsIEEE Geoscience and Remote Sensing Letters, 2011
- The Use of Euclidean Geometric Distance on RGB Color Space for the Classification of Sky and Cloud PatternsJournal of Atmospheric and Oceanic Technology, 2010
- Development of a sky imager for cloud cover assessmentJournal of the Optical Society of America A, 2007
- Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky ImagesJournal of Atmospheric and Oceanic Technology, 2006
- A Simple Method for the Assessment of the Cloud Cover State in High-Latitude Regions by a Ground-Based Digital CameraJournal of Atmospheric and Oceanic Technology, 2006
- Survey over image thresholding techniques and quantitative performance evaluationJournal of Electronic Imaging, 2004
- Blobworld: image segmentation using expectation-maximization and its application to image queryingIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
- A neural network approach to cloud classificationIEEE Transactions on Geoscience and Remote Sensing, 1990