Paper Info
Title
A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images
Abstract
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.
Year
DOI
Venue
2020
10.3390/rs12101619
REMOTE SENSING
Keywords
DocType
Volume
change detection,SAR image,convolutional neural network,multi-scale spatial pooling
Journal
12
Issue
ISSN
Citations 
10
Remote Sens. 2020, 12, 1619
0
PageRank 
References 
Authors
0.34
14
6
Name
Order
Citations
PageRank
Chen Jia-Wei100.34
Wang Rongfang200.34
Ding Fan300.34
Liu Bo400.34
Licheng Jiao55698475.84
Jie Zhang6841185.41