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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (1) : 95-101     DOI: 10.6046/gtzyyg.2018.01.13
Orginal Article |
The method for detecting forest cover change in GF-1images by using KPCA
Lingyu YIN1(), Xianlin QIN1(), Guifen SUN1, Shuchao LIU1, Xiaofeng ZU1, Xiaozhong CHEN2
1. Research Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China
2. ForestInformation Center of Sichuan Province, Chengdu 610081, China
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Abstract  

In order to study the methods for forest cover change monitoring by using GF-1 images, the Yajiang County in Sichuan Province was selected as the research area to extract the information of forest coverage based on the two GF-1 WFV data. Firstly, the data were normalized by using the iteration re-weight multivariate alteration detection(IR-MAD)method. The two images were transformed by kernel principal component analysis(KPCA)method, and formed differencing image. Then, the changed area was extracted using the method of maximum between class variances(OTSU)for automatic threshold selection. Finally, the change detection results were validated using OTSU with the field sample data, and the extracted results were verified by way of precision test, and comparatively analyzed with the change vector analysis(CVA). The research results show that the overall accuracy of the two change detection methods is higher than 80%, and the overall accuracy of the KPCA method is 89.27%. The user precision of unchanged area is 93.88%, and the user's accuracy of changed area is 80.28%. The accuracy of the KPCA method is better than that of the algorithm based on the traditional CVA method. It is shown that, after the data transformation, KPCA algorithm can reduce the correlation between the variables and enhance the signal to noise ratio of the image, thus improving the recognition accuracy for the changed area.
Keywords GF-1      iteration re-weight multivariate alteration detection(IR-MAD)      kernel principal component analysis(KPCA)      method of maximum between class variance(OTSU)     
:  TP79  
Issue Date: 08 February 2018
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Lingyu YIN
Xianlin QIN
Guifen SUN
Shuchao LIU
Xiaofeng ZU
Xiaozhong CHEN
Cite this article:   
Lingyu YIN,Xianlin QIN,Guifen SUN, et al. The method for detecting forest cover change in GF-1images by using KPCA[J]. Remote Sensing for Land & Resources, 2018, 30(1): 95-101.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.13     OR     https://www.gtzyyg.com/EN/Y2018/V30/I1/95
载荷 波段号 谱段范围/μm 空间分辨率/m 幅宽/km 侧摆能力/(°) 重访时间/d
多光谱相机 1
2
3
4		 0.45~0.52
0.52~0.59
0.63~0.69
0.77~0.89		 16 800
(4台相机组合)		 ±35° 4
Tab.1  Parameters for WFV of GF-1 satellite
Fig.1  Scatter plots and regression equations ofno changed pixels in each band
类别 B1 B2 B3 B4
目标影像(2016年) 435.939 4 160.580 9 151.038 7 838.843 8
参考影像(2014年) 113.770 0 143.727 7 189.128 4 901.870 9
辐射归一化后目标影像 113.787 0 143.685 0 189.172 2 902.127 9
Tab.2  Average reflectivity of different bands of target image before and after radiation normalization and reference image
年份 KPCA主成
分序号		 特征值 方差贡献率
/%		 累计方差
贡献率/%
2014 1 481 531.37 93.990 93.990
2 30 326.56 5.920 99.901
3 369.81 0.072 99.982
4 45.89 0.018 100.000
2016 1 433 300.52 91.710 91.710
2 38 609.56 8.170 99.880
3 462.11 0.098 99.978
4 89.24 0.022 100.000
Tab.3  Variance distribution of principal components of GF-1 image KPCA
Fig.2  Local images of east part in YajiangCounty
Fig.3  Binary images of changed areas
Fig.4  Result extractedfrom changed areas
变化检测法 类别 检验样本点/个 列总数 用户
精度/%
变化区 未变化区
KPCA法 变化区 114.00 28.00 142 80.28
未变化区 17.00 261.00 278 93.88
行总数 131.00 289.00 420 ―
生产者精度/% 87.02 90.32 ― ―
总体精度/% 89.27
CVA法 变化区 92.00 31.00 123 74.80
未变化区 39.00 258.00 297 86.87
行总数 131.00 289.00 420 ―
生产者精度/% 70.23 89.28 ― ―
总体精度/% 83.33
Tab.4  Accuracy of two methods for change detection
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