Remote sensing image dense target deep learning detection method

Abstract

The invention discloses a remote sensing image dense target deep learning detection method. The method is used for extracting a remote sensing image dense target. According to the method, an image isinputted into a deep CNN basic network, so that a feature map can be obtained; deep convolution features are inputted into a dense target extraction framework, so as to be subjected to region of interest (RPN branch) extraction, object classification and rectangular frame regression; for RPN branches, a high density bias sampler is adopted to excavate more samples (difficult samples) with high density to improve detection performance; Soft-NMS is employed to reserve more positive objects after the dense target extraction framework; and finally, a refined rectangular frame is outputted, so thatthe quantity of dense objects can be counted.

Description

technical field [0001] The invention belongs to the field of high-resolution remote sensing image recognition, in particular to a deep learning detection method for dense targets in remote sensing images. Background technique [0002] With the rapid development of remote sensing technology, a large number of high-resolution remote sensing images can now be provided. Compared with low-resolution images, high-resolution remote sensing images contain more detailed spatial information, which not only brings opportunities, but also poses challenges to the recognition of remote sensing images. The recognition and analysis based on high-resolution remote sensing image technology has been applied to remote sensing image target detection tasks. Among them, plastic greenhouse detection is a very important research direction. Greenhouse cultivation is the creation of microscopic environments to grow crops such as: vegetables, tobacco and fruits. Monitoring and mapping greenhouse are...

AI Technical Summary

Problems solved by technology

However, it only works on dense targets adjoining surrounding targets

[0004] Although the above methods have greatly improved the performance of dense object detection, it is still difficult to effectively extract extremely dense objects.

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