Method and system for extracting forest land from remote sensing image based on depth learning, storage medium, and electronic device

Abstract

The invention provides a method for extracting forest land from remote sensing images based on depth learning, which comprises the steps of image data fusion, sample making, model training, precisionevaluation and forest land prediction. The invention also relates to a remote sensing image woodland extraction system based on depth learning, a storage medium and an electronic device. The inventionintegrates digital terrain model, DSM data and four-band sample data are fuse, training data having five bands of information is obtained, input to a depth learning network model to obtain a forest land classification model containing DSM features by iterative iteration, Then the boundary extraction network RCF is used to extract the woodland boundary from remote sensing image as a constraint, soas to complete the accurate division of the woodland and make the boundary of the woodland more conform to the reality. The invention realizes automatic and efficient extraction of the woodland vector data, greatly improves the accuracy of the woodland extraction method of the remote sensing image, and shortens the time cost and human cost of manual drawing.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and is a deep learning high spatial resolution remote sensing image woodland extraction method based on a digital surface model, which is mainly applied to the automatic extraction of forest land from sub-meter remote sensing images. Background technique [0002] Forest land plays an indispensable role in the human living environment. It is an important part of the ecological environment, one of the best symbols to reflect the regional ecological environment, and an important object of environmental remote sensing application research. The use of remote sensing technology to extract green forest information has a long history. Remote sensing can quickly and effectively detect the type, characteristics, growth and other information of large-scale forest land; the purpose of forest remote sensing information extraction is to effectively determine the distribution of plants on...

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Problems solved by technology

[0004] Woodlands have obvious spectral characteristics, especially the high reflectance characteristics of green plants in the near-infrared band and the low reflectance characteristics in the red range of visible light, which have become the theoretical basis for constructing vegetation indices, so vegetation spectra have become one of the earliest basic remote sensing research objects Most of the traditional woodland extraction methods are based on the NDVI vegetation index plus a certain texture recognition method. However, due to the different density of the woodland itself and the limitations of traditional methods, the extraction effect has been poor; there are many types of vegetation. Woodland is only one of them. It is difficult to separate the woodland from the vegetation only by using the vegetation index. It is still difficult to separate the woodland from the vegetation. Although there is a certain difference in texture between the woodland and the low grassland, but There are still cases of misrecognition and omission when using semantic segmentation; in addition, due to the relatively rich types of vegetation, there are also many types of shrubs and trees in the forest land category, and the texture characteristics are different, so it is difficult to define

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