Remote Sensing Image Mode Translation by Spatial Disentangled Representation Based GAN

Zishuo, 韩子硕 Han; Chunping, 王春平 Wang; Qiang, 付强 Fu; Bin, 赵斌 Zhao

doi:10.3788/aos202141.0728003

Cited by 3 publications

(1 citation statement)

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“…However, the unique imaging mechanism of SAR and the complex electromagnetic wave scattering process result in a large amount of coherent speckle noise in the collected images, and the ground object information is abstract and poorly readable, while the visible light image is just the opposite, which can present brightly colored ground objects and objects. With high-definition image quality [3], it is extremely difficult to match the two. The first step of data fusion is to match a certain number of image points with the same name for high-precision registration [4], so it is of great significance to study how to achieve high-precision image matching of SAR and optical.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous remote sensing image matching algorithm based on hierarchical feature balanced sampling convolution cross-correlation framework

Zou

Zhu

Chen

2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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Synthetic Aperture Radar (SAR) imaging and visible light imaging are the two most commonly used imaging methods for remote sensing satellites. Since the imaging information of the two is highly complementary, many scenarios of data fusion need to use these two heterogeneous data. However, before data fusion, the data of the two modalities need to be aligned, and the performance of heterogeneous data matching algorithm directly affects the performance of obtaining ground control points during alignment. At present, there are many one-stage and two-stage methods for heterogeneous remote sensing image matching. The existing one-stage methods have problems such as large average prediction offset, low matching accuracy, and imbalance of features at different levels when combining features. The stage method cannot meet the actual needs in terms of speed and accuracy. To address these issues, this paper proposes an end-to-end heterogeneous remote sensing image matching framework HB3CF. The framework uses the classic image feature extraction network to construct a pair of pseudo-twin networks, which extract features from two heterogeneous images respectively. Then, the features of each level are uniformly sampled on the channel using the convolution layer to reduce the weight of the high-dimensional features in the joint features, which effectively improves the expression ability of the features of each level of the model. Finally, the matching results are obtained by performing convolution, cross-correlation and up-sampling operations on the high-dimensional features of the SAR image and the optical image. Experiments show that the average offset error of the model is reduced by about 25% compared to state-of-the-art methods. When the average offset error is less than or equal to 0 pixel, 1 pixel, 2 pixel and 3 pixel, the accuracy is increased by 8.53%, 9.54%, 4.16% and 1.12% respectively, reaching 25.90%, 65.03%, 86.65% and 92.15%, which greatly improves the accuracy of the matching method of heterogeneous images, and explores the application of deep learning methods in large-scale heterogeneous remote sensing image matching tasks.

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Section: Introductionmentioning

confidence: 99%

Heterogeneous remote sensing image matching algorithm based on hierarchical feature balanced sampling convolution cross-correlation framework

Zou

Zhu

Chen

2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

View full text Add to dashboard Cite

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fire-GAN：基于生成对抗网络的火焰图像生成算法

Qin Kui,

Hou Xinguo,

Zhou Feng

et al. 2023

激光与光电子学进展

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基于合成缺陷的偏光片外观对抗异常检测方法

Zhong Xiaopin,

Zhu Junwei,

Lie Zhihao

et al. 2023

激光与光电子学进展

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The current automatic optical detection technology is challenged by the following two aspects: it is difficult to obtain enough defect samples, and the types are extremely unbalanced; the appearance defects are diverse and complex.The above problems seriously affect the detection accuracy and efficiency of the appearance defects of polarizers.Considering these issues, a new depth antagonism method of anomaly detection without real defect samples is proposed.An encoder is used to capture the regular characteristics of the stripestructured light defect image and a decoder is used to reconstruct the defectfree image. An encoder module is then used to form an unsupervised countermeasure network.Finally, the abnormal score is calculated according to the difference between the reconstructed image and the sample image. In the training phase, synthetic defects are added, and the target potential loss function is improved to further increase the detection accuracy. The experimental results for a polarizer appearance defect data setconsidering factors such as light imbalance, noise, and camera distortionshow that the area under curve of the test results of the proposed method reaches 97. 9%, the average detection time of a single image is 19. 2 ms, and the detection accuracy is 94. 6%, which is superior to other methods such as GANomaly. The effectiveness and robustness of the proposed method are verified.

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Remote Sensing Image Mode Translation by Spatial Disentangled Representation Based GAN

Cited by 3 publications

References 0 publications

Heterogeneous remote sensing image matching algorithm based on hierarchical feature balanced sampling convolution cross-correlation framework

Heterogeneous remote sensing image matching algorithm based on hierarchical feature balanced sampling convolution cross-correlation framework

fire-GAN：基于生成对抗网络的火焰图像生成算法

基于合成缺陷的偏光片外观对抗异常检测方法

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