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Remote sensing target detection method based on contourlet grouping characteristic pyramid convolution

A feature pyramid and target detection technology, applied in the field of image processing, can solve the problems of missed detection, blurred edges, small target size, etc., and achieve the effect of rich input, improved detection accuracy, and improved accuracy

Active Publication Date: 2019-08-06
XIDIAN UNIV
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  • Application Information

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Problems solved by technology

However, the disadvantage of this method is that it cannot effectively deal with the blurring of the edge of the ship target caused by the low resolution of the image.
[0005] At present, when the target detection algorithm is used for target detection in low-resolution optical remote sensing images, the problem of small target size and blurred edges exists, and the problem of missing small targets often occurs when detecting targets in optical remote sensing images.

Method used

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  • Remote sensing target detection method based on contourlet grouping characteristic pyramid convolution
  • Remote sensing target detection method based on contourlet grouping characteristic pyramid convolution
  • Remote sensing target detection method based on contourlet grouping characteristic pyramid convolution

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Embodiment 1

[0041] In the prior art, due to the large size of the optical remote sensing image, low resolution, small target size, and blurred edges of the target, the existing methods often cannot learn the characteristics of small targets well when performing target detection on optical remote sensing images. This leads to low accuracy for small targets.

[0042] The present invention has carried out research for this present situation, proposes a kind of remote sensing target detection method of contourlet grouping feature pyramid convolution, see figure 1 , the present invention first uses non-contourlet downsampling transformation for edge enhancement, and uses group convolution and channel breakup technology to enable the network to simultaneously input the pre-enhanced image and the enhanced image and automatically learn the feature combination as the depth The input of the feature pyramid convolutional network, the extracted features are more suitable for target detection tasks, a...

Embodiment 2

[0069] The optical remote sensing image target detection method based on the contourlet grouping feature pyramid convolution is the same as in embodiment 1, figure 2 (a) shows the detailed process of convolution implementation of group convolution in the present invention, figure 2 (b) shows a schematic diagram of the grouped convolution module of the present invention, which uses a 1×1 grouped convolution and a 3×3 separable convolutional convolution kernel to construct a grouped convolution module, and the grouped convolution module It is three layers, and its module structure is: input layer → first 1x1 group convolution → first 3x3 separable convolution layer → second 1x1 group convolution → output layer.

[0070] In this example, the specific parameters of the group convolution module are set as follows:

[0071] The first 1x1 group convolution in the group convolution module, the number of groups is 2, different groups use the image before edge enhancement and the ima...

Embodiment 3

[0078] The remote sensing target detection method based on contourlet grouping feature pyramid convolution is the same as embodiment 1-2, and the residual connection convolution module described in step (4b) refers to: the feature map input layer of the previous stage→the first convolution Layer → second convolutional layer → third convolutional layer → point-by-point addition with output from feature map input layer → current stage feature map output layer.

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Abstract

The invention discloses a remote sensing target detection method based on contourlet grouping characteristic pyramid convolution, which solves the problems of low recall rate and high false alarm rateof an optical remote sensing image aircraft and a ship during edge blur. The method comprises the steps of constructing a test data set; constructing a training data set; performing non-contour downsampling transformation on the data set; constructing a target detection network based on the grouping feature pyramid convolution; using the data set to train a target detection network based on grouping feature pyramid convolution; carrying out target detection on the test data set by using the trained target detection network based on grouping characteristic pyramid convolution; outputting testresults. According to the invention, the non-downsampling contour edge is used to strengthen the image edge feature, and the grouping convolution and feature pyramid network is constructed, so that the method is more suitable for optical remote sensing image target detection, and the accuracy of edge blurred small target detection is obviously improved. The method is used for ground object targetdetection of optical remote sensing images.

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 technology, in particular to an optical remote sensing image target detection method based on contourlet group convolution. The invention can be applied to the ground object detection of aircrafts and ships in different areas of optical remote sensing images. Background technique [0002] Target detection task is an important branch in the field of computer vision. Target detection in remote sensing images refers to using remote sensing images as data acquisition sources, and using image processing algorithms to locate and classify the targets of interest in the images. As a key technology in the application of remote sensing images, remote sensing target detection can be applied in military monitoring, urban planning, agricultural and forestry construction and other fields. It can provide accurate location and categ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V2201/07G06F18/241G06F18/253G06F18/214
Inventor 焦李成李玲玲邹洪斌张梦璇郭雨薇丁静怡王佳宁张丹冯志玺
Owner XIDIAN UNIV
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