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Remote sensing image cloud detection method based on convolution nerve network

A technology of convolutional neural network and remote sensing images, which is applied in the fields of instruments, scene recognition, computing, etc., and can solve problems such as difficulty in adapting to remote sensing images

Active Publication Date: 2016-08-10
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most of the existing cloud detection algorithms only use the low-dimensional features of the image, and it is difficult for them to adapt to remote sensing images with complex backgrounds, especially when there are thin clouds with low contrast to the background in the image.

Method used

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  • Remote sensing image cloud detection method based on convolution nerve network
  • Remote sensing image cloud detection method based on convolution nerve network
  • Remote sensing image cloud detection method based on convolution nerve network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0045] Example 1: An example of remote sensing image cloud detection with a simple background

[0046] Step 1: Establish training sample set

[0047] Firstly, the ground truth is manually marked for the selected sample image, and the cloud region is marked out. Then N sub-blocks of size K×K are selected from the cloud area as positive samples, and 6*N sub-blocks of size K×K are selected from the non-cloud area as negative samples. In this experiment, N takes 42000 and K takes 55.

[0048] Step 2: Convolutional Neural Network Classification Model Generation

[0049] Build a convolutional neural network with 6 layers, where the first 4 layers are convolutional layers and the last 2 layers are fully connected layers. The input of the network is an RGB three-channel remote sensing image sub-block with a size of 55×55, and the output of the network is two values, which respectively represent the probability of being a cloud and the probability of not being a cloud. For the conv...

Embodiment 2

[0067] Example 2: Example of Cloud Detection in Remote Sensing Image with Complicated Background

[0068] At this time, there is no need to perform steps 1 and 2 in Example 1, and the detection results can be obtained by directly performing steps 3 to 6 in Example 1. Figure 3(a) is the original remote sensing image with complex background; Figure 3(b) is the result of superpixel clustering and segmentation using SLIC algorithm; Figure 3(c) is the cloud probability map generated by convolutional neural network; Figure 3 (d) is the cloud detection result map after refining the cloud probability map.

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Abstract

The invention discloses a remote sensing image cloud detection method based on a convolution nerve network, comprising steps of establishing a training sample set, generating a convolution nerve network classification model, extracting a super-resolution sub-area, generating a cloud probability graph, performing rough detection on a cloud area, and performing fine detection on the cloud area. Through the above arrangement, the remote sensing image cloud detection method can accurately detect the cloud area in the remote sensing image, and, for the remote sensing image having the complex background or the semitransparent cloud, can obtain a better detection result. As a result, the remote sensing image cloud detection method can solve problems of false determination and analysis which are caused by the cloud, and can facilitate the follow-up processing and analysis. The remote sensing image cloud detection method enables the cloud area in the cloud detection result to have a better rim, to have better robustness in a complex environment and can obtain a better detection result.

Description

technical field [0001] The invention provides a remote sensing image cloud detection method based on a convolutional neural network, which belongs to the field of optical remote sensing image processing. Background technique [0002] With the rapid development of remote sensing technology, remote sensing images have been widely used in many fields such as surveying, geographic mapping and resource monitoring. However, under normal circumstances, there will be clouds in remote sensing images, which will not only cause the loss of collected information, but also bring difficulties to subsequent processing such as target detection or target recognition, so that analysts can get wrong analysis results. Therefore, cloud detection and cloud removal have become one of the most important problems to be solved in remote sensing image processing. [0003] At present, cloud detection methods in remote sensing images mainly include: segmentation method based on physical threshold and p...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62
CPCG06V20/13G06V10/267G06F18/2321
Inventor 谢凤英资粤史蒙云姜志国尹继豪史振威张浩鹏
Owner BEIHANG UNIV
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