Ground object classifying method and device

A ground object classification and ground object technology, applied in the field of remote sensing, can solve the problems of high computational cost, limited hyperspectral image description, inability to effectively distinguish different ground objects, etc., to achieve the effect of improving accuracy and reducing quantity requirements.

Inactive Publication Date: 2017-10-10
HUNAN UNIV
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Problems solved by technology

However, hyperspectral images cannot well solve the problems of building shadows and cloud coverage in complex urban areas. In addition, when classifying objects in more complex urban areas, hyperspectral images cannot effectively distinguish different objects composed of the same material. Therefore, it is necessary to extract more separable spatial spectral features
[0003] Spatial structure features based on morphological attribute profiles can effectively extract multi-scale structural information in hyperspectral images, but due to the complexity and diversity of hyperspectral images, a single feature can only describe hyperspectral images. In the identification and classification of , it is difficult to obtain enough training samples, and the calculation cost is high

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  • Ground object classifying method and device

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no. 1 example

[0032] Please refer to figure 2 , figure 2 It shows the flow chart of the ground object classification method provided by the preferred embodiment of the present invention. The ground object classification method includes the following steps:

[0033] Step S101, extracting multiple attribute profile features of the hyperspectral image to obtain a first image.

[0034]In the embodiment of the present invention, the first image may be the extended morphological multi-attribute profile feature of the hyperspectral image, and the extended morphological multi-attribute profile feature of the hyperspectral image may be extracted by using the morphological attribute profile operation. Multiple attributes can be calculated from hyperspectral images, including area attributes, moment of inertia attributes, standard deviation attributes, and so on.

[0035] As an implementation, the method for extracting multiple attribute profile features of a hyperspectral image to obtain the fir...

no. 2 example

[0095] Please refer to Figure 8 , Figure 8 A schematic block diagram of the object classification apparatus 200 provided by the embodiment of the present invention is shown. The feature classification device 200 includes a first extraction module 201 , a second extraction module 202 , an image fusion module 203 and a feature classification module 204 .

[0096] The first extraction module 201 is configured to extract multiple attribute profile features of the hyperspectral image to obtain the first image.

[0097] In the embodiment of the present invention, the first extracting module 201 may be used to execute step S101.

[0098] Please refer to Figure 9 , Figure 9 for Figure 8 A schematic block diagram of the first extraction module 201 in the object classification apparatus 200 is shown. The first extraction module 201 includes an image acquisition unit 2011 , a principal component analysis unit 2012 , an execution unit 2013 and a first image acquisition unit 201...

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Abstract

The invention relates to the technical field of remote sensing, and provides a ground object classifying method and device. The ground object classifying method includes the steps that a plurality of attribute section characteristics of a hyperspectral image are extracted to obtaina first image; attribute section characteristics of a laser scanning image are extracted to obtain a second image; the first image and the second image are fused to obtain a third image; a preset convolutional neural network is adopted to conduct characteristic extraction and classification on the third image to obtain ground object classifying results. According to the ground object classifying method and device, abundant spectral information of the hyperspectral image and accurate elevation information of the laser scanning image are complementary, and the problem is solved that due to inaccurate spectral information, the ground object classification is limited. Besides, the convolutional neural network is adopted to conduct the characteristic extraction and the classification, the requirements on the number of training samples are reduced, and at the same time, the accuracy of the ground object classification is improved.

Description

technical field [0001] The invention relates to the field of remote sensing technology, in particular to a method and device for classifying ground objects. Background technique [0002] Hyperspectral imagery is a cutting-edge technology in the field of remote sensing, which can acquire hundreds of spectrally continuous bands. Compared with panchromatic and multi-spectral remote sensing images, hyperspectral images have higher spectral resolution and can provide richer object information, thereby better identifying object objects. However, hyperspectral images cannot well solve the problems of building shadows and cloud coverage in complex urban areas. In addition, when classifying objects in more complex urban areas, hyperspectral images cannot effectively distinguish different objects composed of the same material. Ground objects, therefore, need to extract more separable spatial spectral features. [0003] Spatial structure features based on morphological attribute prof...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/176G06V10/40G06F18/24
Inventor 李树涛郝乔波康旭东
Owner HUNAN UNIV
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