Buradasınız

Anasayfa » Content

Accuracy Assessment of Cloud Reconstruction Approaches using Segmentation

Journal Name:

  • International Journal of Innovation and Applied Studies

Publication Year:

  • 2013

Key Words:

Author Name
Abstract (2. Language): 
Cloud is the major obstacle to analyze data in the satellite images. The various approaches are used to remove the cloud from the satellite image for further processing. The approaches are in-painting and multi-temporal. But, the algorithm working for these approaches cannot produce the accurate results. So, that the accuracy assessment helps to motivate the increased accuracy result. The main aim of this paper is to analyze the accuracy of in-paint and multi-temporal approach and produce the pros and cons of those approaches. Accuracy assessment helps to obtain degree of truthfulness of the results. There are ‘n’ numbers of metrics are available to find the accuracy of the result such as analyzing variance, spatial error, probabilistic error etc. In this paper, two approaches are implemented and the results are applied to the segmentation algorithm. Then, the segmentation results are analyzed by using the error matrix. The error matrix have constructed based on the difference between the clusters of the image result. For segmentation the K-Means algorithm is used and for simplicity only two clusters are segmented. Segmentation result will clearly show that the accuracy of the in-paint and multitemporal approaches. From the result it is evident that the multi-temporal approach produces a better result when compared to the in-painting. Especially, in that multi-temporal the Averaging method produces better accuracy result.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon ijias-13-067-35.pdf
  • 1
112-115
  • English

REFERENCES

References: 

[1] Ana Carolina Siravenha, Danilo Sousa, Aline Bispo, and Evaldo Pelaes, “Evaluating Inpainting Methods to the Satellite
Images Clouds and Shadows Removing”, Communications in Computer and Information Science Volume 260, 2011, pp.
56-65.
[2] Anil Z Chitade and Dr. S.K. Katiyar, “Colour Based Image Segmentation Using K-Means clustering”, International Journal
of Engineering Science and Technology, Vol. 2(10), 2010, 5319-5325.
[3] Asmala Ahmad, Shaun Quegan, “Cloud Masking for Remotely Sensed Data using Spectral and Principal Components
Analysis”, Engineering, Technology & Applied Science Research, Vol. 2, No. 3, 2012, 221-225.
[4] Chao-Hung Lin, Po-Hung Tsai, Kang-Hua Lai, and Jyun-Yuan Chen, “Cloud Removal from Multi-temporal Satellite Images
Using Information Cloning”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 1, January 2013.
[5] Michael J. Wilson and Lazaros Oreopoulos, “Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data
Continuity Mission”, IEEE Transaction On Geoscience And Remote Sensing. Vol. 51, No. 2, 2013, 0196-2892.
[6] Qingmin Meng, Bruce E. Borders, Chris J. Cieszewski, and Marguerite Madden, “Closest Spectral Fit for Removing Clouds
and Cloud Shadows”, Photogrammetric Engineering & Remote Sensing, Vol. 75, No. 5, pp. 569–576.
[7] Nicolas Champion, “Automatic cloud detection from multi-temporal satellite images: towards the use of pléiades time
series”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIXB3,
2012 XXII ISPRS Congress, Melbourne, Australia.

Thank you for copying data from http://www.arastirmax.com