High spatial resolution remote sensing image transfer learning classification method based on OpenStreetMap

Abstract

The invention discloses a high spatial resolution remote sensing image transfer learning classification method based on OpenStreet Map. The high spatial resolution remote sensing image transfer learning classification method comprises the following steps: automatically generating an image object sample set of a target domain based on OpenStreet Map data; adopting a source domain image with the same imaging sensor as the target domain image, and automatically generating an image object sample set of the source domain based on the historical classification map; synthesizing the target domain sample set and the source domain sample set to form a mixed sample set for training a transfer learning algorithm classifier based on a random forest; and adopting the final classifier to predict the target domain image object type, so that a final classification result can be obtained. Under the condition that the target domain image category is not manually marked, the high spatial resolution remote sensing image transfer learning classification method can extract a label of a target domain image object from OpenStreet Map data, and, by mining historical classification image information of thesame sensor image and combining with a target domain image sample set, adopts a transfer learning algorithm to classify the images, thus reducing the classification cost, and the high spatial resolution remote sensing image transfer learning classification method can be applied to large-range high-spatial-resolution remote sensing image classification work.

Description

technical field [0001] The invention belongs to the field of remote sensing image processing, in particular, relates to a high spatial resolution remote sensing image transfer learning classification method based on OpenStreetMap (OSM), the method can obtain a target domain sample set based on OpenStreetMap, combined with a source domain sample set, A transfer learning algorithm is used to classify high spatial resolution remote sensing images. Background technique [0002] Land surface information obtained from high spatial resolution remote sensing images can be used in urban planning, land monitoring and other industries. At present, obtaining surface type information from high-spatial-resolution remote sensing images is mainly divided into two categories: unsupervised classification and supervised classification. Supervised classification needs to prepare a sample set for classifier training in advance, while unsupervised classification does not require prior knowledge ...

AI Technical Summary

Problems solved by technology

For example, this type of method first needs to solve the problem of spatial position registration between map data and images. Spatial position offset often leads to the generation of wrong samples; the second problem is that open source map data like OpenStreetMap relies on public contributions. The completeness of data varies greatly in different regions. For example, the data completeness is higher in the coastal cities in the east of my country than in the west.

These problems bring challenges to directly rely on OpenStreetMap data to generate sample sets for classification

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