Abstract
The presence of missing values in a geostatistics dataset can affect the performance of using those dataset as generic purposed. In this paper, we have developed a novel method to estimate missing observation in geostatistics by using sigma-delta modulation type of Discrete-Time Cellular Neural Networks(DT-CNN). The nearest neighboring pixels of missing values in an image are used. The interpolation process is done by using B-template with Gaussian filter. The DT-CNN is used for reconstructing the imputed values from analog image value to digital image value. We have evaluated this approach through the experiments on geostatistics image which has different characteristics of missing pixels such as Landsat 7 ETM+ SLC-off and standard geostatistics image. The experimental results show that by using sigma-delta modulation type of Discrete-Time Cellular Neural Networks, we can achieve a high PSNR for various image datasets and at different characteristics of missing image.
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Landsat Information, http://eros.usgs.gov//Find_Data/Products_and_Data_Available/ETM
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Prasomphan, S., Aomori, H., Tanaka, M. (2011). Design of DT-CNN for Imputing Data at Unobserved Location of Geostatistics Image Dataset. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_24
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DOI: https://doi.org/10.1007/978-3-642-27183-0_24
Publisher Name: Springer, Berlin, Heidelberg
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