结合纹理特征分析与比辐射率估计的震后滑坡提取
Post-seismic landslide extraction by combining texture analysis and emissivity estimating
- 2018年22卷第S1期
纸质出版日期: 2018
DOI: 10.11834/jrs.20187261
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纸质出版日期: 2018 ,
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杨明生, 李山山, 冯钟葵. 结合纹理特征分析与比辐射率估计的震后滑坡提取[J]. 遥感学报, 2018,22(S1):223.
Post-seismic landslide extraction by combining texture analysis and emissivity estimating[J]. Journal of Remote Sensing, 2018,22(S1):223.
滑坡提取是获取滑坡灾情信息的首要环节。针对背景复杂的震后灾区,为了更加有效地增强裸地信息并降低土壤背景差异对NDVI植土分离能力的影响,利用NDVI进行滑坡提取时结合纹理特征分析和比辐射率估计来提高滑坡提取的精度。首先,根据纹理特征差异将主导背景因子不同的区域划分为独立的处理单元,以避免全局滑坡提取所出现的过提取或欠提取现象;然后,利用NDVI估算比辐射率,以增强裸地信息;最后,通过阈值分割实现更高精度的滑坡提取。围绕5·12大地震后的汶川县城及其周边区域,采用Landsat 5 TM和Terra ASTER数据展开实验,该方法的滑坡提取结果与人工提取结果吻合度较好,Kappa系数分别为0.8531和0.9271,优于基于NDVI的全局阈值分割法和其他一些典型的监督分类法。实验结果表明,在背景复杂的灾区环境下,结合纹理特征分析和比辐射率估计的滑坡提取方法能明显降低漏检率和错检率,进而有效提高滑坡提取的精度,且对中等分辨率的不同遥感数据源的适用性较强。
Landslides are the most common geological disasters that result from large seismic activities and heavy rainfall in mountainous areas. They are destructive events that occur suddenly and have a wide distribution range. Landslide extraction is the primary factor in collecting destruction information and plays a key role in disaster prevention and emergency rescue. This paper presents a method that combines texture analysis and emissivity estimation to extract landslides from different backgrounds. Many researchers have focused on landslide extraction and recognition
and they proposed methods based on the ability of the Normalized Difference Vegetation Index (NDVI) to separate vegetation. However
although NDVI easily extracts landslides from high vegetation areas
it is affected by soil interference in sparse vegetation areas. Thus
extracting landslides is difficult. An NDVI-based method is proposed for landslide extraction in a complicated environment and to make NDVI effective in sparse vegetation zones.In accordance with the research area and remote sensing data
this method consists of four steps on the assumption that image preprocessing has been completed. First
the NDVI image is calculated by using near-infrared and red bands; this approach shows that some local regions have a sharp contrast
whereas others have a weak contrast. Second
texture analysis is conducted to divide the research area into several blocks according to this contrast distribution. NDVI mean and near-infrared Angular Second Moment (ASM) based on Gray-Level Co-occurrence Matrix (GLCM) could be selected as the texture feature to segment zones easily by providing simple thresholds. A continual large vegetation area is outstanding in the Mean feature
and a mountainous landslide area is rough in the ASM feature. The remaining area
except the first two
varies in the ASM feature related to soil components. Thus
the combination of Mean and ASM features facilitates texture analysis. Third
emissivity is estimated in different blocks based on the NDVI image. In this section
the NDVIs of pure soil and pure vegetation from statistics of the NDVI image in different blocks are critical parameters in calculating the Percentage of Vegetation (PV)
thereby obtaining the emissivity. In a limited area
emissivity contrasts within different objects
especially for soil and vegetation
thereby making it more suitable than NDVI for landslide extraction. Finally
the landslides are extracted through emissivity threshold segmentation technology with an appropriate threshold manually.Landsat 5 TM and Terra ASTER data are tested by this method
and the result is favorable given that it is more consistent with that of artificial landslide extraction than that of the other methods
such as maximum likelihood supervised classification
neural net supervised classification
support vector machine-based supervised classification
and NDVI global threshold segmentation. For objective and quantitative evaluation
the result of artificial extraction is considered as the ground-truth data to calculate the confusion matrix of other results
with the Kappa coefficient being used to demonstrate the performance of each method. As a result
the method described in this article achieves a high Kappa coefficient
namely
0.8531 (TM) and 0.9271 (ASTER). By contrast
maximum likelihood classification achieves 0.7634 (TM)
neural net classification achieves 0.66 (TM)
SVM-based classification achieves 0.6896 (TM)
NDVI global threshold (0.3) segmentation achieves 0.622 (TM)
and NDVI global threshold (0.5) segmentation achieves 0.7487% (TM). Evidently
this method can effectively eliminate omission and misclassification. With the increase in the resolution of remote sensing data
ASTER (15 m) provides a better result compared with TM (30 m)
thereby showing that this method does not rely on data sources
and a high resolution contributes to a good result.Comparative analysis between different methods and data sources indicates the following results: The NDVI mean and near-infrared ASM are good texture features for separating different background environment blocks
especially with soil as the leading factor. Emissivity reflects the spatial difference in objects
although it is estimated by NDVI. The estimated emissivity not only separates vegetation from NDVI but also increases the difference between soils
thereby resulting in good landslide extraction. The advantages of texture analysis and emissivity estimation enhance landslide extraction in complicated research areas. Finally
the threshold selection problem in NDVI global threshold segmentation method is solved
and the sample selection and learning process in supervised classification are avoided. This method is designed for a medium resolution that ranges between 10 m to 50 m; thus
this method can be used with other data sources with a similar resolution
such as Landsat 5 TM and Terra ASTER. We proposed an effective method for landslide extraction for medium resolution. To handle high-resolution remote sensing data
this method will be combined with object-oriented methods in follow-up studies for accurate landslide extraction. The proposed method has a strong dependency on manual threshold selection; this dependency is inconvenient in the auto-extraction process. Therefore
self-adaptive threshold extraction is another challenge in future work.
滑坡提取比辐射率纹理分析灾害遥感复杂背景阈值分割
landslide extractionEmissivityTexture analysisdisaster remote sensingdiverse backgroundthreshold segmentation
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