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Remote sensing image thin and weak target segmentation method

A remote sensing image and target segmentation technology, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as poor recognition accuracy of thin targets in remote sensing images, inconspicuous features of thin targets in remote sensing images, and unbalanced categories. , to improve the accuracy of network segmentation, avoid the decline of learning speed, and achieve the effect of accurate segmentation effect.

Inactive Publication Date: 2020-01-14
HARBIN ENG UNIV
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Problems solved by technology

[0003] However, the above deep learning networks all require large-scale data sets and accurate manual labeling, and all face the problems of indistinct features of subtle targets in remote sensing images, unbalanced categories, and large background interference. Poor target recognition accuracy

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  • Remote sensing image thin and weak target segmentation method
  • Remote sensing image thin and weak target segmentation method

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Embodiment Construction

[0045] The present invention will be further described through actual cases below in conjunction with the accompanying drawings, but the scope of the invention will not be limited in any way. The present invention proposes a new method for precise segmentation of remote sensing image faint targets based on deep learning theory, and the specific network model used is shown in Figure 1. attached figure 2 It is the overall flowchart of the inventive method, and the present invention mainly comprises the following steps:

[0046] Step 1: For the input original image, a series of data preprocessing methods such as random cropping, size scaling, angle transformation, affine transformation, random noise addition, filtering processing, and brightness change are first used to perform image enhancement and data expansion on the original image. At the same time, because the number of weak targets in the data samples is often uneven in different backgrounds, and background interference i...

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Abstract

The invention provides a remote sensing image thin and weak target segmentation method. Firstly, data enhancement and corresponding preprocessing are carried out on an original remote sensing image, U-net is improved by means of the dense connection thought of DenseNet, and a Dense-Unet network structure is provided. Dense convolution is used in a network structure, the cascade relation between convolution channels is enhanced, through a symmetric structure and a jump connection thought, the connection between features of all layers is further tighter, and thin and weak target features can belearned more effectively. In order to ensure the real-time performance of final network identification and reduce the parameter quantity, a bottleneck layer and a batch normalization layer are introduced behind each dense block. And the objective function is adjusted by using the cost-sensitive vector weight, so that the problem of unbalanced segmentation target categories is solved, and the segmentation precision is further improved. And finally, a plurality of independent models are trained by using an ensemble learning method, the independent models are combined, and target category information is jointly predicted in the picture.

Description

technical field [0001] The invention relates to a digital image processing method, in particular to a method for accurately segmenting faint targets in remote sensing images. Background technique [0002] Remote sensing image target segmentation is an important technical method for remote sensing image target recognition, which is widely used in many fields such as environmental assessment, traffic planning, and automatic driving. Semantic segmentation of images is the key to understanding image information. The basic principle is to segment pixels into different regions according to the semantic meaning expressed in the image, that is, to recognize the image at the pixel level and mark the object category of each pixel. With the rapid development of remote sensing technology, high-resolution remote sensing satellite images have the characteristics of wide observation range, containing more object information, and difficult to extract information features. When traditional ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06N3/08G06N3/04G06N20/20G06K9/62
CPCG06T7/11G06N3/08G06N20/20G06T2207/20084G06T2207/20081G06T2207/10032G06N3/045G06F18/214G06F18/24
Inventor 于淼叶秀芬刘文智郭书祥马兴龙
Owner HARBIN ENG UNIV
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