Remote sensing image dense target deep learning detection method

A technology of dense targets and remote sensing images, applied in the field of high-resolution remote sensing image recognition, can solve the problem that extremely dense targets are difficult to be effectively extracted, and achieve the effect of ground object positioning and high-precision ground object positioning.

Active Publication Date: 2020-05-08
WUHAN UNIV
View PDF4 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it only works on dense targets adjoining surrounding targets
[0004] Although the above methods have greatly...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Remote sensing image dense target deep learning detection method
  • Remote sensing image dense target deep learning detection method
  • Remote sensing image dense target deep learning detection method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] Below by embodiment, further illustrate outstanding feature and remarkable progress of the present invention, only in order to illustrate the present invention and in no way limit the present invention.

[0027] The embodiment of the present invention provides a remote sensing image dense target deep learning detection method, which specifically includes the following steps:

[0028] (1) Using self-labeled high-spatial-resolution remote sensing image dense greenhouse object detection data set (GHDOERS), the GH DOERS training data set contains 1290 Google Earth images, and the test set and verification set are 430 and 862 images respectively. is 512x512 pixels. The dataset contains data from 6 provinces and regions across the country, including: Hubei Province, Liaoning Province, Shandong Province, Xinjiang Uygur Autonomous Region, Shaanxi Province, and Jiangsu Province.

[0029] 1.1. Select the training set and test set TrainA and TestB in the data set for the sample d...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06K9/32G06N3/04
CPCG06V20/13G06V10/25G06V2201/07G06N3/045G06F18/241G06F18/214Y02A40/25
Inventor 马爱龙陈鼎元钟燕飞郑卓
Owner WUHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap