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A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
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Remote Sens. 2020, 12(10), 1619; https://doi.org/10.3390/rs12101619
Received: 19 April 2020 / Revised: 15 May 2020 / Accepted: 17 May 2020 / Published: 19 May 2020
(This article belongs to the Section Remote Sensing Image Processing)
In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutional network with only mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels are equipped in a convolutional network to exploit the spatial context information on changed regions from the difference image. Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset. We compare the proposed method with other state-of-arts and the comparisons are performed on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that our proposed method obtain comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms other state-of-art methods, especially on the Sendai-A and Sendai-B datasets with more complex scenes. More important, MSSP-Net is more efficient than S-PCA-Net and convolutional neural networks (CNN) with less executing time in both training and testing phases. View Full-Text
Keywords: change detection; SAR image; convolutional neural network; multi-scale spatial pooling change detection; SAR image; convolutional neural network; multi-scale spatial pooling
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MDPI and ACS Style

Chen, J.-W.; Wang, R.; Ding, F.; Liu, B.; Jiao, L.; Zhang, J. A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images. Remote Sens. 2020, 12, 1619.

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