Abstract
It is difficult to process and analyze remote sensing owning to cloudy and strips in the image, so we propose a new method to reduce cloud in space-borne image with multi-temporal data based on wavelet transformation. The images are decomposed into low frequency information and high frequency information using Mallet algorithm. Normalized difference low frequency index between two temporal images with cloud is used to reduce the low frequency information because the clouds are low frequency information and they mainly appear in low frequency wavelet coefficients, and higher frequency information of image is also extracted from two high frequency information. The reconstructed image using reduced low frequency and extracted by high frequency wavelet transformation is a new image without cloud. Two real space-borne images with clouds are processed using the proposed method and the result demonstrates that the method is feasible. Stripes also appear in some space-borne images and they are high infrequency information, so they clearly appear in wavelet field, and we reduce high infrequency of strips in wavelet field. The striped image is decomposed into several layers using wavelet transformation, and the strips are appear clearly as lines in high frequency block in each layer. Projection histogram is used to reduce the lines in high frequency block The reduced higher frequency information ant low information are reconstructed into a new image without stripes. We use the wavelet transform method to reduce TM space-borne image with strips, and the processed result shows that wavelet transform is a better method.
Published Version
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