Object Detection Method of Optical Remote Sensing Image Based on Dense Object Feature Learning

Abstract

The invention discloses an optical remote sensing image target detection method based on dense target feature learning, which mainly solves the problem in the prior art that small target information is filtered out due to deep convolution. The specific steps of the present invention are as follows: (1) build a dense target feature network with 25 layers in total and set parameters for each layer; (2) construct a training sample set and a training class label set; (3) obtain the deep and shallow features of the dense target feature network ;(4) Fuse the deep and shallow features of the dense target feature network; (5) Obtain the target candidate frame feature set; (6) Perform dense pooling; (7) Construct a test sample set; (8) Test the test sample set . The invention has the advantages of good extraction of deep and shallow features of optical remote sensing images and high precision of target detection.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an optical remote sensing image target detection method based on dense target feature learning in the technical field of optical remote sensing image target detection. The present invention can be applied to identify and detect ground objects in different regions of an optical remote sensing image. Background technique [0002] Target detection technology is one of the core issues in the field of computer vision. Remote sensing target detection uses images captured by remote sensing satellites as data sources, and uses image processing technology to locate and classify objects of interest in images. Remote sensing target detection is an important part of remote sensing application technology. It can capture attack targets and provide accurate location information in high-tech military confrontation, which is of great significance in the military field. [0003] Wi...

AI Technical Summary

Problems solved by technology

This method can accurately and richly represent the features of the target, and can well extract the target candidate frame, and reduce the extraction time of the candidate frame by sharing the convolution parameters. The information is filtered out after convolution layer feature extraction and ROI pooling in the region of interest. Only larger-scale targets can be detected, and the detection accuracy for dense small targets is low.

This method assists the detection of remote sensing image features by training a classifier to separate sea and land, which can predict the results of robust target detection and reduce the missed detection of ships caused by side-by-side placement. However, this method still has shortcomings. The disadvantage is that due to the need to use multi-layer convolution to obtain feature maps and predict targets with deep convolution, the multi-scale features of the target are not considered, resulting in insufficient utilization of target information, resulting in unsatisfactory detection of dense small targets

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