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Open AccessArticle

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

1
College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2
Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion, Chongqing 400044, China
3
Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
4
UK Centre for Ecology & Hydrology, Library Avenue, Lancaster LA1 4AP, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(3), 548; https://doi.org/10.3390/rs12030548
Received: 15 December 2019 / Revised: 29 January 2020 / Accepted: 4 February 2020 / Published: 7 February 2020
Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery. View Full-Text
Keywords: synthetic aperture radar (SAR); change detection; deep learning; superpixel synthetic aperture radar (SAR); change detection; deep learning; superpixel
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MDPI and ACS Style

Zhang, X.; Liu, G.; Zhang, C.; Atkinson, P.M.; Tan, X.; Jian, X.; Zhou, X.; Li, Y. Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection. Remote Sens. 2020, 12, 548.

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