Object-oriented timing sequence remote sensing image cloud coverage area automatic detection method

Abstract

The invention provides an object-oriented timing sequence remote sensing image cloud coverage area automatic detection method, comprising six steps of data preparation, image registering, multiscale image segmentation, radiation difference elimination, spectrum similarity calculation, threshold segmentation and detection result generation. The method employs an object-oriented interpretation method, and can effectively perform pattern classification on a cloud coverage area, fully utilize timing sequence information in timing sequence images, and effectively utilize the multi-feature advantage among the timing sequence images; in a timing sequence remote sensing image cloud coverage area detection process, the method employs a multi-feature fusion strategy, fully utilizes timing sequence and spectrum information, and makes detection results more reliable and stable.

Description

technical field [0001] The invention relates to the field of automatic and precise detection of cloud coverage areas of remote sensing images, in particular to an object-oriented automatic detection method of cloud coverage areas of time-series remote sensing images. Background technique [0002] With the continuous accumulation of multi-temporal remote sensing data and the successive establishment of spatial databases, how to extract and detect specific ground object information from these remote sensing data has become an important research topic in remote sensing science and geographic information science. According to remote sensing images of different time phases in the same area, dynamic information such as cities and environments can be extracted to provide scientific decision-making basis for resource management and planning, environmental protection and other departments. However, in areas with complex terrain, such as the mountainous areas in Southwest my country, ...

AI Technical Summary

Problems solved by technology

Although these detection methods have certain potential, the complexity of the cloud coverage area and the lack of prior information of the target cause these algorithms to have certain limitations.

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