Satellite imagery is very useful in information\r\nacquisition of the earth''s surface image, especially the earth''s\r\nresources. However, in the process of retrieval information from\r\nsatellite imagery is often found barriers that can obscure or even\r\ncover the imaging of an area. One of these barriers is a cloud,\r\nwhich result the image that covered with lots of noise. Wavelet\r\ntransformation was usually used to enhance the image or to\r\neliminate striping noise on satellite image. In this paper is used\r\nDiscrete Haar Wavelet transformation to reduce cloud noise on\r\nLandsat TM image. The process includes the Haar Wavelet\r\ndecomposition of image rows and columns. After that,\r\nthresholding process is also applied for de-noising. Thresholding\r\nresults are then reconstructed using the Inverse Discrete Haar\r\nWavelet. The method is applied to the variation of the band\r\nimage, the type of thresholding (hard and soft), as well as the size\r\nof the image convolution. The testing results on the band 1 to\r\nband 6 of Landsat TM imagery showed that the lowest error\r\nvalues are calculated by RMSE (Root Mean Square Error)\r\npresent in band 1. Image signal to noise ratio in band 1 has the\r\nhighest value, which means most high-power image signal to\r\nnoise. This mean that band 1 has the highest pixel value\r\nsimilarity between whole testing data.
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