Texture has a wide range of applications in the field of image processing. The application of existing texture research methods in DEM is mostly texture feature extraction and index calculation. However, the formation of landforms is caused by terrain relief. The existing texture analysis methods generally have a deficiency in the reflection on the human visual system. In this paper, the visual boundary lines, visual outline lines, and skeleton lines were used as the DEM relief textures that can visually reflect terrain relief. Then the extraction method is refined in conjunction with digital terrain analysis theory. Finally, a multi-dimensional visualization model of DEM relief texture was built, which achieves a better visual effect and provides new ideas for digital terrain analysis, and also has a wide range of popularization and application value.
Terrain texture attributes are an important means to characterize different types of landform units. The combination of geomorphic units reflects the spatial distribution pattern of regional geomorphic features. Therefore, the analysis of terrain texture significance and variability is a key link in the study of regional geomorphological features. In the process of extracting and analyzing regional geomorphological features, traditional analysis methods of macroscopic and microscopic morphological indicators rely on window areas and calculation models. Although the morphological feature element analysis method can reveal the basic pattern of regional geomorphology, it is aimed at the spatial change structure of terrain elements. In order to grasp the overall and local hierarchical structure characteristics of regional geomorphology, we propose a comprehensive metric model of terrain texture based on texture feature entropy weight method in this paper. Nine single geomorphological research areas were selected to carry out experimental research in Shaanxi Province, China. The results show that the regional characteristics mapped by the comprehensive metric model of terrain texture show the similarity of similar landforms and the difference of different landforms at a certain scale. The closer the metric model index value is to 1, the more significant the comprehensive feature of texture is, the stronger the periodicity of landform is, and the more uneven the spatial distribution of texture is. This model is helpful to promote the overall and systematic research on digital terrain analysis of regional geomorphology in China.
Fast and accurate sea ice detection is of great significance to the natural environmental protection of the sea and the development of the marine economy. With the development of satellite remote sensing technology, sea ice detection based on high-resolution SAR images has received wide attention. Given the serious pretzel phenomenon of image-level classification results and the limitation of object-level classification by segmentation scale, this study proposes a sea ice extraction method based on the coherence and magnitude information of TanDEM-X images and combining image level and object level. The method was compared with the sea ice extraction results of the traditional method, and the results showed that the overall accuracy, user accuracy, product accuracy, and Kappa coefficient of this newly constructed sea ice extraction method improved 38.74%, 19.43%, 37.48% and 0.7597, respectively, compared with the traditional extraction method, which significantly improved the sea ice extraction accuracy.
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