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.

landslide extraction;Emissivity;Texture analysis;disaster remote sensing;diverse background;threshold segmentation