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Remote-sensing image object detection method based on deep learning

A remote sensing image and target detection technology, applied in the field of panchromatic sharpening processing and target detection, can solve complex, difficult to learn classification models, time-consuming and other problems, and achieve strong universality, low time and calculation redundancy , the effect of enriching spectral information

Active Publication Date: 2018-09-21
TIANJIN UNIV
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Problems solved by technology

[0008] However, the above requirements are undoubtedly complex and time-consuming, and strongly depend on professional knowledge and the characteristics of the data itself. In addition, it is difficult to learn an effective classification model from large-scale data to fully exploit the interaction between large-scale data. associate

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  • Remote-sensing image object detection method based on deep learning
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Embodiment Construction

[0023] In order to make the technical solutions of the present invention clearer, the specific embodiments of the present invention are further described below. like figure 1 As shown, the present invention is implemented according to the following steps:

[0024] 1. Build a large-scale remote sensing image dataset

[0025] The invention selects remote sensing image sets such as SpaceNet on AWS, NWPU VHR-10, and USGS that are published on the Internet to construct the data set of the detection task.

[0026] The NWPU VHR-10 dataset is a publicly available ten-category geospatial object detection dataset. The ten categories of objects are aircraft, ships, oil storage tanks, ports, and bridges, etc., and contain high-resolution images and label files of objects in the drawings and their annotations.

[0027] SpaceNet is a large-scale remote sensing image dataset hosted on Amazon's AWS cloud service platform. It was jointly completed by DigitalGlobe, CosmiQ Works and NVIDIA. I...

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Abstract

The invention relates to a remote-sensing image object detection method based on deep learning. The method comprises: using remote-sensing images to construct a related data set, namely an image dataset after classification and labeling on the remote-sensing images and class labels generated through labeling work; building a panchromatic sharpening model based on generative adversarial networks (GAN); building an object detection model based on a deep convolutional neural network, and carrying out end-to-end training on the model through methods of back propagation, random gradient descent and the like; and carrying out end-to-end testing on the built model. The method of the invention has the advantage of high accuracy.

Description

Technical field [0001] The invention relates to the fields of remote sensing image processing, deep learning, pattern recognition and other fields, and in particular to a method based on deep learning and using a generative adversarial network to perform full-color sharpening processing and target detection on spectral images. Background technique [0002] Due to limitations of signal transmission bands and imaging sensor storage, most remote sensing satellites only provide multispectral (MSI) images with high spectral resolution and panchromatic (PAN) images with high spatial resolution. The complementary advantages of the two images are used to fuse them into a fused remote sensing image with clear spatial details and rich spectral information. This fusion technology is also called full-color sharpening technology. [0003] Currently, the mainstream panchromatic sharpening methods in the field of remote sensing include component replacement method, multi-scale analysis met...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/214G06F18/241
Inventor 侯春萍夏晗杨阳管岱莫晓蕾
Owner TIANJIN UNIV
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