NOISE FILTERING OF REMOTELY SENSED IMAGES USING HYBRID WAVELET AND CURVELET TRANSFORM APPROACH

This article presents a hybrid technique for noise filtering of remotely sensed images based on multiresolution analysis (MRA). Multiresolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image interpretation. Further, noise being one of the biggest problems in image analysis and interpretation for further processing, is effectively handled by multiresolution methods. The paper proposes a hybrid scheme based on wavelet and curvelet transforms on high resolution multispectral images acquired by the Quickbird and medium resolution Landsat Thematic Mapper satellite systems. By comparative analysis, the hybrid approach of curvelet and wavelet for heterogeneous and homogeneous areas has proved to be better than the others. Results are illustrated using Quickbird and Landsat images for proposed method and compared with wavelets and curvelet based noise filtering.