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A multi-temporal SAR image change detection method based on deep learning

A technology of image change detection and deep learning, which is applied in the field of image detection, can solve problems such as the introduction of uncertainty in classification, and achieve the effects of suppressing speckle noise, reducing uncertainty, and suppressing human interference

Active Publication Date: 2022-02-01
CHONGQING UNIV
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Problems solved by technology

However, this pixel-centric rectangular patch processing introduces artifacts on the boundary of the classification patch, which often introduces uncertainty in the classification

Method used

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  • A multi-temporal SAR image change detection method based on deep learning
  • A multi-temporal SAR image change detection method based on deep learning
  • A multi-temporal SAR image change detection method based on deep learning

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Embodiment Construction

[0057] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0058] Such as figure 1 As shown, the present invention discloses a multi-temporal SAR image change detection method based on deep learning, including:

[0059] S1. Obtain the SAR image I of the first moment of detection target 1 and the SAR image I at the second moment 2 , I 1 and I 2 The dimensions are M×N;

[0060] S2, to I 1 and I 2 Perform superpixel segmentation to get I 1 and I 2 superpixel block, I 1 and I 2 The corresponding superpixel blocks of are equal;

[0061] Equal superpixel blocks mean that the corresponding superpixel blocks have the same shape, corresponding position, and number of pixels contained in the block. Because we first 1 Perform superpixel segmentation, and then use I 1 The split mode to split the I 2 .

[0062] S3. Reshaping the superpixel block to obtain a superpixel vector;

[0063] S4. Generate a superpixel ve...

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Abstract

The invention discloses a multi-temporal SAR image change detection method based on deep learning. Compared with the prior art, the superpixel is used as a unit to bring neighborhood information into classification and clustering, which suppresses the generation of rectangular patches. Artificial interference reduces the uncertainty of clustering and also suppresses the speckle noise that affects the interpretability of SAR images. Moreover, the present invention regards change detection as a two-stage classification, and suppresses a large number of false alarms caused by speckle noise. In the first stage, we simply aggregate DI into changing and non-changing classes. In the second stage, based on the intrinsic difference between the variation caused by speckle noise and the variation of real objects, we employ Low-Rank Sparse Decomposition (LRSD) for preprocessing. The low-rank term of LRSD restores the spurious changes caused by speckle noise to its original state, while the sparse term separates speckle noise from the image, greatly weakening the influence of speckle noise on subsequent classification.

Description

technical field [0001] The present invention relates to the field of image detection, in particular to a multi-temporal SAR image change detection method based on deep learning. Background technique [0002] In the past few decades, synthetic aperture radar (SAR) images have attracted extensive attention in the fields of military affairs, environmental monitoring, and urban planning due to the fact that they are not limited by time and weather. One common use of this is change detection. Given two SAR images acquired from the same observation area at different times, the purpose of change detection is to identify the differences between them. We classify change detection methods into two categories according to whether there is a difference image DI (difference image). Post-classification comparison is to directly analyze the changed and unchanged regions in the two images that have been independently classified before analysis, thus avoiding the influence of radiation nor...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/13G06V10/774G06V10/764G06V10/82G06T7/246G06T7/11G06K9/62
CPCG06T7/246G06T7/11G06T2207/10044G06T2207/20081G06T2207/20084G06V20/13G06F18/24G06F18/214
Inventor 张新征刘过苏杭李道通周喜川
Owner CHONGQING UNIV
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