A Neural Network-Based Rotation Difference Correction Method for Multimodal Remote Sensing Images
A neural network and remote sensing image technology, applied in the field of remote sensing image processing, can solve the problems of inaccurate geographic information, unusable, lost algorithms, etc., and achieve the effect of simplifying network structure, enhancing universality, and eliminating correction errors.
A neural network and remote sensing image technology, applied in the field of remote sensing image processing, can solve the problems of inaccurate geographic information, unusable, lost algorithms, etc., and achieve the effect of simplifying network structure, enhancing universality, and eliminating correction errors.
CN113066015BActive Publication Date: 2022-06-03TSINGHUA UNIV +1
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[0046] S4: Based on the predicted difference angle, complete the correction of the multimodal remote sensing image rotation difference angle.
[0049]
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[0061] [E
[0062]=[I(x, y)*L
[0064]
[0068] b=∑
[0070]
[0074]
[0075] traverse all the pixel points of the image in turn, according to the size of the direction angle of its phase consistency, take the feature value as the weight,
[0077] n
[0078] Finally, a rotation feature vector Rot with a size of 1 × 360 is obtained
[0082] The various embodiments in this specification are described in a progressive manner, and what each embodiment focuses on is that it is related to other
[0083] The foregoing description of the disclosed embodiments enables any person skilled in the art to make or use the present invention.
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The invention discloses a neural network-based method for correcting rotation differences of multimodal remote sensing images. It includes the following steps: S1: Obtain a group of multimodal remote sensing image pairs with the same target scene, and perform image preprocessing; S2: For each preprocessed image, calculate phase consistency eigenvalues and orientation angles, and according to the phase Consistency eigenvalues and orientation angles to calculate the rotation feature vector of the image; S3: use the rotation feature vectors of the two images as the input of the neural network, and calculate and output the predicted difference angle of the two images; S4: Based on the predicted difference angle, complete multiple Modal remote sensing image rotation difference angle correction. The invention effectively solves the technical problem of quickly and accurately predicting the rotation difference angle between multimodal remote sensing images in the case of only simple image data information, and gets rid of the limitation of auxiliary geographic space information.
Description
A Rotation Difference Correction Method for Multimodal Remote Sensing Image Based on Neural Network technical field The present invention relates to the technical field of remote sensing image processing, more specifically to the matching of multimodal remote sensing images Preprocessing method for coarse calibration of rotational differences. Background technique Multimodal remote sensing image matching is an important research task in the field of remote sensing image processing technology. application prospects. Achieving accurate registration between remote sensing images is helpful for combining different types of remote sensing images from different imaging sources and at different times. The intrinsic information of the data is correlated to improve the usability of joint imagery from multiple perspectives. However, geometric differences and nonlinear radiation distortion are the key difficulties that restrict the registration accuracy of remote sensing image...
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Patent Timeline
03 Jun 2022
Publication
CN113066015B
- IPC
- G06T3/60; G06T5/00; G06N3/04; G06N3/08
- CPC
- G06T3/608; G06N3/08; G06T2207/10032; G06N3/047; G06N3/045; G06T5/80
- Inventors
- 黄翊航; 张海涛