Mr. Yuanqi Su Profile

Yuanqi Su

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Publications (1)

Proceedings Article | 12 October 2022 Paper

A parallel change detection method for spatiotemporally multi-temporal SAR image based on Q-Learning and Wavelet

Jinxi Peng, Yuanqi Su, Xiaorong Xue, Donghong Song, Zhengbo Xie, Xiaoyong Xue, Wenlong Fan

Proceedings Volume 12342, 123423L (2022) https://doi.org/10.1117/12.2644622

KEYWORDS: Data modeling, Synthetic aperture radar, Remote sensing, Statistical modeling, Wavelets, Parallel computing, Image enhancement, Autoregressive models, Wavelet transforms, Statistical analysis

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In order to make full use of multi-source remote sensing image resources in the application of remote sensing image change detection, a parallel computing for coarse-grained computing data is a very important choice. For the large-scale explosive growth of remote sensing image change which is contained coherent speckles detection sensor data, it’s the amount of data calculation is large and time-consuming. With the development of modern remote sensing big data and Artificial Intelligence (AI), however, it is difficult for traditional methods to solve an efficient algorithm for change detection in synthetic aperture radar application. In connection with acquiring SAR (Polarimetric Synthetic Aperture Radar) Image to accurately obtain an effectively computing power. Looking for data processing and task computing as the basis for parallelization, we propose a parallel change detection method for multi-temporal SAR images based on wavelet transform, and comprehensively apply the Q-learning model of parallel computing with intelligent processing. Firstly, according to the statistical features of SAR images and the semantics of Convolutional Neural Network Pixel (CNN-Pixel) analysis for an efficient change detection method based on Q-Learning semantic analysis and wavelet transform is proposed. Secondly, on the basis of Pixel-Data is achieved the accuracy of change detection, probabilistic and statistical conjugate multi-distribution function features, which is pixels and tasks are pre-processed, pixel-space data calculation of Gaussian mixture model and conjugate gradient transform task model for parallel calculation. Thirdly, the change-sequence data with obstacles at the time series model of Q-learning in the changing target object data on which the surrounding path is an obstacle to achieving that image-pixel sequence data of conjugate gradient calculation data and threshold iteratively optimizes the feature extraction of the changing area, the multi-sample training set is obtained, and then the Bellman equation model is established to calculate the statistical features including the obstacle for SAR. Image change detection. The experiments are show that the parallel change detection method and computing power will achieve better results, and the Q-learning method of behavioral science is widely used in remote sensing applications to solve the application of local area change detection in images. Enhancement-Learning platform is adopted to find the best fitness of the threshold change computing precision data and task hybrid parallel computing model about it.

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