Method for detecting spissatus and spissatus shadow based on Landsat thematic mapper (TM) images and Landsat enhanced thematic mapper (ETM) images

Abstract

The invention discloses a method for detecting spissatus and spissatus shadow Landsat thematic mapper (TM) images and Landsat enhanced thematic mapper (ETM) images. The method comprises steps of dividing an input image into 16 sub-atlases and performing wiener filtering denoising and normalization for the 16 sub-atlases; performing rough detection for the spissatus and the shadow in the 16 sub-atlases and selecting reference pairs from a rough detection result; solving a mass center connection inclined angle and a space of a final reference pair in accordance with all reference pairs; matching the spissatus and the shadow in the 16 sub-atlases in accordance with the mass center connection inclined angle and the space of the final reference pair and performing supplementary detection for unmatched spissatus and unmatched shadow; adding a matching result of the spissatus and the shadow and a supplementary detection result and obtaining final detection result sub-images of all sub-atlases; and sequentially splicing final detection result sub-images of all sub-atlases and obtaining a final detection result image. Auxiliary information and manual intervention are not required, the detection precision is high and the method can be used in detection and classification of remote sensing image variation and pre-processing of image segmentation.

Description

Technical field [0001] The invention belongs to the technical field of optical remote sensing image processing, and relates to the detection of thick clouds and shadows of thematic mapper TM image and enhanced thematic mapper ETM image, and can be used for preprocessing of remote sensing image change detection, classification and image segmentation. Background technique [0002] With the continuous development of space technology, the use of medium and high resolution remote sensing images for land resource survey, change detection, vegetation and water monitoring has become more and more popular. Due to the complex and diverse weather conditions, satellites rarely encounter complete clear and cloudless conditions in a large area when acquiring remote sensing images. The existence of cloud makes it impossible for people to obtain the real surface information under the cloud coverage area from the image, which will cause difficulties for some subsequent segmentation, classificatio...

AI Technical Summary

Problems solved by technology

This method combines the spatial relationship of imaging and pixel neighborhood information while considering the spectral information, but the algorithm has a scaling effect, and it is only applicable to FY-3-A or HJ-1-A images, and cannot be directly used for special topics Thick cloud and shadow detection in Mapper TM images or Enhanced Thematic Mapper ETM images

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