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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (1) : 72-77     DOI: 10.6046/gtzyyg.2018.01.10
Orginal Article |
Active deep learning based polarimetric SAR image classification
Jia XU1,4(), Chunqi YUAN1,2, Yuane CHENG3, chenyu ZENG5, Kang XU4
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
2. North Information Control Group, Nanjing 211153, China
3. Jiangsu Province Surveying & Mapping Research Institute, Nanjing 210013, China;
4. Jiangsu Province Surveying & Mapping Engineering Institute, Nanjing 210013, China;
5. School of Software Central South University, Changsha 410075, China
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Abstract  

Supervised classification methods usually require adequate labeled samples which are difficult and time-consuming to obtain for polarimetric SAR images, while the expression capability of the shallow structure learning algorithm is limited. A novel supervised classification method for polarimetric SAR imagery based on active deep learning is proposed in this paper. Firstly, the features are extracted from an original image by multiple polarization target decomposition methods for fully describing the data,and the features which are separable and invariable can be extracted with unsupervised learning by auto-encoder. Then, the initial classifier is trained and fine-tune the whole model with a small number of labeled samples. Finally, the most valuable samples (the largest ambiguity samples for classifier)are selected to label by active learning. Experimental results in comparison with conventional methods for polarimetric SAR data sets of RADARSAT-2 and EMISAR show that the proposed method can achieve higher classification accuracy with a small number of labeled samples.
Keywords polarimetric SAR      target decomposition      image classification      active learning      deep learning     
:  TP751.1  
Issue Date: 08 February 2018
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Jia XU
Chunqi YUAN
Yuane CHENG
chenyu ZENG
Kang XU
Cite this article:   
Jia XU,Chunqi YUAN,Yuane CHENG, et al. Active deep learning based polarimetric SAR image classification[J]. Remote Sensing for Land & Resources, 2018, 30(1): 72-77.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.10     OR     https://www.gtzyyg.com/EN/Y2018/V30/I1/72
分解方法 极化参数 维数
Pauli Pauli_a,Pauli_b,Pauli_c 3
Krogager Krogager_KS,Krogager_KD,Krogager_KH 3
Huynen Huynen_T11,Huynen_T22,Huynen_T33 3
Bames Bames_T11,Bames_T22,Bames_T33 3
Yamaguchi Yamaguchi_Vol ,Yamaguchi_Odd
Yamaguchi_Dbl ,Yamaguchi_Hlx		 4
Cloude Cloude_T11,Cloude_T22,Cloude_T33 3
Freeman Freeman_Vol,Freeman_Odd,Freeman_Dbl 3
Holm Holm_T11,Holm_T22,Holm_T33 3
Tab.1  Polarimetric decomposition features
Fig.1  Sketch map of classification based on auto-encoder and softmax
Fig.2  Flowchart of polarimetric SAR image cassification based on active deep learning
Fig.3  Experimental data
每类样本数目 RADARSAT-2 EMISAR
SVM Deep SVM Deep
5 66.58 69.93 78.29 82.14
10 72.31 75.39 81.23 86.23
15 78.20 80.27 84.48 89.39
20 80.93 81.15 87.30 90.10
25 81.19 82.23 88.64 90.61
30 81.94 82.90 89.72 90.94
35 82.18 83.22 90.46 91.82
40 82.34 83.33 90.95 91.90
Tab.2  Classification accuracy for SVM and Deep based methods of different scale samples random selected(%)
Fig.4  Classification accuracy for SVM and Deep based on active learning
Fig.5  Classification results comparison of different methods for RADARSAT-2 data
Fig.6  Classification results comparison of different methods for EMISAR data
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