Image Lineament Enhancement Technique Based On Wavelet Transform And Block Directional Filter

Remote sensing was successfully used in many fields due to its synoptic view over large areas, enabling the record of surface morphology and tectonic patterns. With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites, the fusion of digital image data has become a valuable tool in remote sensing image evaluation. By merging different images, the user can take advantage of the unique characteristics of each particular data set. In recent years, many image fusions have been developed, such as high pass filter image fusion, IHS image fusion, wavelet image fusion, and IHS image fusion based on wavelet transform etc. Linear feature or the edge of object is very important in remote sensing interpreting, and enhance the linear feature can facilitate the interpretation.The author analyzes the characteristics of the most used image fusion methods. The IHS fusion based wavelet transform can reduce the color distortion and improve the fusion quality. High pass filter fusion can enhance the particular oriental linear features. Two new image fusion methods based on wavelet transform and high pass filter are proposed to enhance the linear features and fusion quality in order to facilitate the visual interpretation. To decide the direction of the image linear features, the author uses hough transform to detect the line features of the image and get their direction, In practice, the author divides the image into small blocks, for each block applies hough transform to decide the linear feature or edge direction. In the fusion processing, uses the result of the block hough transform to decide the directional filter of each block.Two ETM+ images are fused with this proposed approach. The fusion results are compared with the images processed by recent fusion methods. The analysis results demonstrate the proposed fusion approach does better in enhance the linear features or edges. And the result images are also good at color representation and spatial detail enhancement. Compare with high-resolution image and topographic map indicates that the enhanced information reflects the actual surface morphology.