SEGMENTATION BASED CLOUD AND CLOUD SHADOW DETECTION IN SATELLITE IMAGERY
One of the main source of noises in remote sensing satellite imagery is regional clouds and shadows of these clouds caused by atmospheric conditions. In many studies, these clouds and shadows are masked with multi-temporal imagery taken from the same area to decrease effects of misclassification and deficiency in different image processing techniques, such as change detection and NDVI (Normalized Difference Vegetation Index). This problem is surpassed in many studies by mosaicking with different images obtained from different acquisition dates of the same region. The main step of all studies that cover cloud cloning or cloud detection is the detection of clouds from a satellite image. In this study, clouds and shadow patches are classified by using a spectral feature based rule set created after segmentation process of Landsat 8 image. Not only spectral characteristics but also structural parameters like pattern, area and dimension are used to detect clouds and shadows. Rule set of classification is developed within a transferable approach to reach a scene independent method. Results are tested with different satellite imageries from different areas to test transferability and compared with other state-of art methods in the literature.
 Zhu, Z., & Woodcock, C. E. (2012). “Object-based cloud and cloud shadow detection in Landsat imagery”. Remote Sensing of Environment, 118, 83–94.
 Arvidson, T., Gasch, J., & Goward, S. N. (2001). “Landsat 7’s long-term acquisition plan - An innovative approach to building a global imagery archive”. Remote Sensing of Environment, 78(1-2), 13–26.
 Irish, R. R. (2000). “Landsat 7 automatic cloud cover assessment”. In S. S. Shen & M. R. Descour (Eds.), AeroSense 2000 (Vol. 4049, pp. 348–355).
 Zhang, X., Qin, F., & Qin, Y. (2010). “Study on the Thick Cloud Removal Method Based on Multi-Temporal Remote Sensing Images”. 2010 International Conference on Multimedia Technology (pp. 1–3). IEEE.
 Huang, C., Thomas, N., Goward, S. N., Masek, J. G., Zhu, Z., Townshend, J. R. G., & Vogelmann, J. E. (2010). “Automated masking of cloud and cloud shadow for forest change analysis using Landsat images”. International Journal of Remote Sensing, 31(20), 5449–5464.
 Simpson, J. J., & Gobat, J. I. (1995). “Improved cloud detection in GOES scenes over the oceans”. Remote Sensing of Environment, 52(2), 79–94.
 Amato, U., Antoniadis, A., Cuomo, V., Cutillo, L., Franzese, M., Murino, L., & Serio, C. (2008). “Statistical cloud detection from SEVIRI multispectral images”. Remote Sensing of Environment, 112(3), 750–766.
 Irish, R. R., Barker, J. L., Goward, S. N., & Arvidson, T. (2006). “Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm”. Photogrammetric Engineering & Remote Sensing, 72(10), 1179–1188.
 Saunders, R. W., & Kriebel, K. T. (1988). “An improved method for detecting clear sky and cloudy radiances from AVHRR data”. International Journal of Remote Sensing, 9(1), 123–150.
 Gao, B.-C., & Kaufman, Y. J. (1995). “Selection of the 1.375-µm MODIS Channel for Remote Sensing of Cirrus Clouds and Stratospheric Aerosols from Space”. Journal of the Atmospheric Sciences, 52(23), 4231–4237.
 Gao, B.-C., Kaufman, Y. J., Han, W., & Wiscombe, W. J. (1998). “Corection of thin cirrus path radiances in the 0.4–1.0 μm spectral region using the sensitive 1.375 μm cirrus detecting channel”. Journal of Geophysical Research, 103(D24), 32,169–32,176.
 Gao, B. C., Yang, P., Han, W., Li, R. R., & Wiscombe, W. J. (2002). “An algorithm using visible and 1.38-??m channels to retrieve cirrus cloud reflectances from aircraft and satellite data”. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1659–1668.
 Hutchison, K. D., Mahoney, R. L., Vermote, E. F., Kopp, T. J., Jackson, J. M., Sei, A., & Iisager, B. D. (2009). “A geometry-based approach to identifying cloud shadows in the VIIRS cloud mask algorithm for NPOESS”. Journal of Atmospheric and Oceanic Technology, 26(7), 1388–1397.
 Le Hégarat-Mascle, S., & André, C. (2009).” Use of Markov Random Fields for automatic cloud/shadow detection on high resolution optical images”. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), 351–366.
 Berendes, T., Sengupta, S. K., Welch, R. M., Wielicki, B. a., & Navar, M. (1992). “Cumulus cloud base height estimation from high spatial resolution Landsat data: a Hough transform approach”. IEEE Transactions on Geoscience and Remote Sensing, 30(3), 430–443.
 Simpson, J. J., & Stitt, J. R. (1998). “A procedure for the detection and removal of cloud shadow from AVHRR data over land”. IEEE Transactions on Geoscience and Remote Sensing, 36(3), 880–897.
 Simpson, J. J., Jin, Z., & Stitt, J. R. (2000). “Cloud shadow detection under arbitrary viewing and illumination conditions”. IEEE Transactions on Geoscience and Remote Sensing, 38(2 II), 972–976.
 USGS. (2015). “Landsat 8 (L8) Data Users Handbook”. Earth Resources Observation and Science (EROS) Center, 8(June), 97.
 Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). “SLIC superpixels compared to state-of-the-art superpixel methods”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2281.
 Lloyd, S. (1982). “Least squares quantization in PCM”. IEEE Transactions on Information Theory, 28(2), 129–137.
 Vedaldi, A., & Fulkerson, B. (2010). “Vlfeat: an open and portable library of computer vision algorithms”. Proceedings of the international conference on Multimedia - MM ’10 (p. 1469). New York, New York, USA: ACM Press.
 Hall, D. K., & Riggs, G. A. (2011). “Normalized-Difference Snow Index (NDSI)”. Encyclopedia of Snow, Ice and Glaciers (pp. 779–780).
 Gao, Bo-cai. (1996). “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3): 257–66.
The manuscript with title and authors is being submitted for publication in Journal of Aeronautics and Space Technologies. This article or a major portion of it was not published, not accepted and not submitted for publication elsewhere. If accepted for publication, I hereby grant the unlimited and all copyright privileges to Journal of Aeronautics and Space Technologies.
I declare that I am the responsible writer on behalf of all authors.