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Active and passive remote sensing data fusion classification method based on a fuzzy evidence theory

A technology of evidence theory and remote sensing data, applied in the field of remote sensing surveying and mapping, can solve the problems of unbalanced classification, insufficient information mining, large classification difference, etc., and achieve the effect of improving accuracy.

Active Publication Date: 2019-05-17
HENAN POLYTECHNIC UNIV
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Problems solved by technology

[0005] In order to solve some deficiencies in the existing classification technology, the present invention provides a fusion classification method of active and passive remote sensing data based on fuzzy evidence theory, which solves the problems of insufficient information mining, unbalanced classification and classification differences in the spatial dimension of active and passive remote sensing data. Larger problems, effectively improving the accuracy and efficiency of classification, providing a good reference value for researchers

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  • Active and passive remote sensing data fusion classification method based on a fuzzy evidence theory
  • Active and passive remote sensing data fusion classification method based on a fuzzy evidence theory
  • Active and passive remote sensing data fusion classification method based on a fuzzy evidence theory

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Embodiment 1

[0043] Such as figure 1 -The active and passive remote sensing data fusion classification method based on fuzzy evidence theory described in 2, comprises the following steps:

[0044] S1, laser point cloud stitching, firstly, the laser radar is used to perform remote sensing measurement in the area to be observed, and reversely generate the laser point cloud of the appearance of the area to be observed, and then stitch each laser point cloud to generate the laser point cloud data of the surface appearance of the area to be observed, Then save the generated laser point cloud data of the surface appearance of the area to be observed, and copy at least one copy as the original laser point cloud for backup;

[0045] S2, point cloud gridding, first obtain the original laser point cloud in step S1, respectively generate point cloud horizontal plane coordinates (X, Y) and grid coordinates (i, j), and make point cloud horizontal plane coordinates (X, Y ) and grid coordinates (i, j) e...

Embodiment 2

[0072] Such as figure 1 Shown in -5, in order to prove the accuracy and high-efficiency of this method, the airborne laser radar point cloud and image data of the Fayingen region in Germany provided by ISPRS are based on experimental data, and the present invention is described in detail:

[0073] S1, laser point cloud stitching, the airborne laser point cloud data in the Vaihingen area is acquired by the ALS50 system of Leica Company, the field of view is 45 degrees, the flight altitude is 500m, there are a total of 10 strips, and the strips overlap The rate is 30 degrees, and the average point cloud density is 6.7points / m2. Multiple echoes and intensity information are recorded in the point cloud data. Due to the season, the trees are not so luxuriant, and the multiple echo information of the point cloud is weak. Before the data was released, strip correction had been performed on the original point cloud data, and the systematic error was eliminated. After strip correction...

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Abstract

The invention relates to an active and passive remote sensing data fusion classification method based on a fuzzy evidence theory, which comprises five steps of laser point cloud splicing, point cloudgridding, vegetation area identification, grid data segmentation and evidence theory classification. Compared with other classification methods, the method is simple; The method combines airborne laser point cloud and aerial image active and passive remote sensing data, more effective features are fused as much as possible for ground object classification, multi-source features are fused in combination with a fuzzy evidence theory method, and the final joint probability of each ground object is obtained, so that the ground object classification precision can be effectively improved.

Description

technical field [0001] The invention relates to an active and passive remote sensing data fusion classification method based on fuzzy evidence theory, and belongs to the technical field of remote sensing surveying and mapping. Background technique [0002] Driven by the national urban strategy, my country's urbanization is developing rapidly. In 2011, the urbanization rate exceeded 50%. In 2015, the urbanization rate reached 56.10%. challenge. With the continuous update of sensors and image processing methods, remote sensing technology has become the main means of urban management and planning. Different remote sensing data can be used to classify large-scale urban areas, so as to provide three-dimensional reconstruction of the city, geographical monitoring, smart Cities and urban planning and management provide effective data support, which has always been a research hotspot in photogrammetry and remote sensing. In recent years, many scholars have done extensive and in-dep...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62
Inventor 赵宗泽王双亭王宏涛都伟冰王春阳
Owner HENAN POLYTECHNIC UNIV
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