A Method for Automatic Semantic Segmentation of Mining Areas in Remote Sensing Images

A remote sensing image and semantic segmentation technology, applied in the field of automatic target detection and deep learning, can solve the problems of low efficiency and achieve high work efficiency and high accuracy

Active Publication Date: 2021-08-13
合肥深蓝空间智能科技有限公司
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for automatic semantic segmentation of mining areas in remote sensing images, to solve the problem of low efficiency of the existing methods proposed in the above-mentioned background technology

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  • A Method for Automatic Semantic Segmentation of Mining Areas in Remote Sensing Images

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Embodiment Construction

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] see Figure 1-3 , The present invention provides a technical solution: a method for automatic semantic segmentation of mining areas in remote sensing images, the specific steps are as follows:

[0030] Step 1. Create a training sample set

[0031] (1) Obtain remote sensing images of the mining area, and manually outline the boundary of the mining area to form a boundary grid file;

[0032] (2) Using ArcGIS to generate fishing...

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Abstract

The invention discloses a method for automatic semantic segmentation of mining areas in remote sensing images in the technical field of automatic target detection and deep learning. Outline the boundary of the mining area, form a boundary raster file, use ArcGIS to generate fishing nets of 448*448 and 512*512 scales, use the two generated fishing nets to batch crop the remote sensing images of the mining area, and generate different The size of the image block, as the input data of the deep learning network, cuts the mining area grid boundary file in the image through the fishing net, and generates the corresponding boundary file of each mining area image block, as the label data of the network; the present invention uses a combination of The network Den-Res Net can highly abstract the extracted features while retaining the integrity of the features. It can be used to solve the feature redundancy problem of the Dense Net network. It has high work efficiency and can automatically perform semantic separation with high accuracy.

Description

technical field [0001] The invention relates to the technical field of automatic target detection and deep learning, in particular to a method for automatic semantic segmentation of mining areas in remote sensing images. Background technique [0002] Semantic segmentation of mining targets in multi-source and multi-temporal remote sensing images of a certain place can quickly and intuitively detect its temporal and spatial changes, which is conducive to analyzing the characteristics of temporal and spatial changes in mining areas, and provides timely and objective information for the sustainable development of mineral resources. , Accurate technical support and detailed scientific information, and also provide a scientific basis for the governance and transformation of mining cities and the planning and construction of "green cities". Therefore, the semantic segmentation of image mining targets is of great significance. [0003] At present, there are mainly visual interpret...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/13G06F18/241G06F18/214
Inventor 吴艳兰杨辉殷志祥
Owner 合肥深蓝空间智能科技有限公司
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