An aerial photograph image difference detection method based on a dual network

Abstract

The invention provides an aerial photograph image difference detection method based on a dual network, comprising four parts of image acquisition and processing, building a dual network model, training the dual network model and using the model. As the depth learning is not required to design feature manually, the problem that it is difficult to select an effective feature descriptor in image segmentation and difference detection can be avoided. The depth learning method can also overcome the shortcomings of low robustness to illumination in RGB image difference detection task. At the same time, the invention adopt the object detection instead of the traditional segmentation method, which can more distinguish the single object, and the position coordinate of the object is more accurate andeasier to express. By calculating the ROI of the prediction box after detection to determine the relevant objects of the two images, it can also reduce the requirements of registration accuracy. Importantly, the invention adds the semantic information of the object, has the object class information, has stronger anti-interference ability, and can better analyze the difference types.

Description

technical field [0001] The invention belongs to the field of image processing and machine vision, and relates to a method for detecting differences of aerial images based on double networks. Background technique [0002] In recent years, with the rapid development of drones, drone aerial photography has been widely used in fields such as agronomy, geology, forests, oceans, geographic surveying and mapping, military reconnaissance, and environmental protection. We measure the earth with unprecedented precision in terms of time and space, and collect various data quickly and accurately. In the past, data was acquired from the air, usually by using satellites or aircraft. But compared to these two, UAV is a superior "air sensor". The line of sight of satellites is blocked by clouds covering more than 2 / 3 of the earth, and drones can collect data more accurately and more frequently; compared with airplanes, drones are cheaper, easier to operate, and safer. UAVs can provide hi...

AI Technical Summary

Problems solved by technology

The present invention adds deep learning to difference detection, since deep learning does not require manual design of features, it can avoid the problem of difficult selection of effective feature descriptors in image segmentation and difference detection

Images