Satellite-borne target fusion detection method based on rotating region convolutional neural network

Abstract

The invention discloses a satellite-borne target fusion detection method based on a rotating region convolutional neural network, and relates to the field of satellite-borne target detection. The invention is oriented to intelligent computing platforms such as a universal parallel computing architecture processor, a field programmable gate array, a system-on-chip (SOC) and the like. The method comprises the following steps: firstly, fusing a panchromatic (PAN) image and a multispectral (MS) image by adopting a panchromatic sharpening method, and then detecting a target in the fused spaceborneimage by adopting a target detection framework based on a rotating region convolutional neural network, wherein the target detection framework is based on a Faster R-CNN structure. According to the invention, the detection accuracy of the small target in the multi-source satellite-borne image is improved, the target detection framework has good generalization ability, and the method can be widelyapplied to the fields of target detection, safety monitoring and the like.

Description

technical field [0001] The invention relates to the field of spaceborne target detection, in particular to a spaceborne target fusion detection method based on a rotating area convolutional neural network. Background technique [0002] Object detection is a popular direction in computer vision and digital image processing. It is widely used in robot navigation, intelligent video surveillance, industrial inspection, aerospace and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision. Therefore, target detection has become a research hotspot in theory and application in recent years. It is an important branch of image processing and computer vision, and it is also the core part of an intelligent monitoring system. At the same time, target detection is also a basic algorithm for target recognition. It plays a vital role. Since AlexNet, the Convolutional Neural Networks (CNN) model built by Hinton's researc...

AI Technical Summary

Problems solved by technology

Therefore, there are four main issues that the algorithm needs to consider: 1. Satellite images usually have high resolution, which brings difficulty to target detection; 2. Different from commonly used data sets such as PASCAL-VOC and ImageNet, in satellite image data sets Objects such as ships, small vehicles, and airplanes may be very small (less than 15×15 pixels) and dense; 3. The relative lack of public data sets; 4. The problem of complete rotation invariance, many target objects, such as cars, boats and airplanes, etc., There are many directions when looking down

Images