Texture Analysis Based Research On Terrain Morphology Characteristics

Under the inner and outer geological forces, terrain surfaces show the complex morphological characteristics. The research on analysis and quantification of morphology and spatial structure of terrain surface not only play an important role in national economy, but also have actual values in scientific research on landform formation, development and evolution. The existing researches of morphological characteristics of landform mostly discuss landform changes recur to the spatial variable characteristics of topographic factors on micro scale, which can well describe the local morphological characteristics of terrain surface. But the knowledge of morphological characteristics of landform on macro scale is still insufficiency. And recently effective analysis methods of morphological and structural characteristics of landform on macro scale are still in shortage.In this paper, the theories and methods of image texture analysis and computer vision are introduced into DEM based digital terrain analysis. Texture analysis methods are used and improved to reveal the morphological and structural characteristics of landform on macro scale, which can be recognized as the new digital terrain analysis methods and ideas to the quantification and classification of landform morphological characteristics. The results show that it has the important academic significance and practical value in deepening the cognition of such basic theory questions as digital terrain modeling and analysis, expanding digital terrain analysis methods on the macro-level, improving the knowledge on morphological and structural characteristics of terrain surface and deepening the use of DEM based terrain analysis in the national economy construction.The mainly contents and research achievements of this paper are as follows:(1) According to the controversy of exist texture conception, basing on the basic characteristic of terrain gradient and mutation, this paper introduces a compositive explanation of texture which combines the statistics characteristics of texture with structure characteristics. Such basic attributes of texture as periodicity, directivity, randomness and scale dependence are discussed.(2) Basing on the human vision perception mechanism, this paper presents a multi-level texture analysis method which towards landform morphology cognition. This method simulates the visual perception process of landform and describes the morphological and spatial structural characteristics from global statistical features to local spatial relationship, from invariant features to spatial heterogeneity, from single-scale features to multi-scale features. Experiments show that this multi-layer progressive based texture analysis and quantitative method can effectively reveal the morphological and spatial structural features of land form at multi levels.(3) This paper designs a series of texture quantitative models and indicators layer by layer according to multi-level texture analysis method. Hu invariant moment model, spatial gray-level co-occurrence matrix model (GLCM), improved three dimensional lacunarity model (3D-LCA) and Daubechies-4 wavelet decomposition model are selected and improved to quantify the morphological and spatial structural features of landform. Then 11 typical landform sample areas in Shannxi Province are used as a case study for exploring the applicability of texture analysis models on multi levels in describing the morphological and spatial structural features in terms of the directivity, scale invariance, rotation invariance, analysis range of models and DEM resolution based scale dependency.(4) In view of multi-level properties of texture analysis models and hierarchical properties of DEM data, this paper studies the terrain recognition of 11 classical landform types in Shannxi Province based on the spatial domain and frequency domain characteristics of terrain surface. In virtue of layer-by-layer classification strategy, feature classification indexes of different terrain types have high significance. The accuracy of classification based on wavelet transformation in intermediate frequency component is higher than the results based on low frequency component. The highest classification accuracy is up to 88.68%. Affected by the boundary effects and other factors, wavelet decomposition levels are not absolutely matching the classification accuracy with linear relationship. The optimal decomposition levels of 25m DEM on a scale of 1:50000 are 2. and the eigenvector of classification is formed by 9 elements.On the basis of the above research, this paper explores a new research method of landform morphology cognitive, analysis, quantization and application based on texture analysis. The results show that texture analysis starts from human vision perceptive mechanism can effectively analyze and quantify the morphological and structural characteristics of terrain surface at multi levels of terrain morphology. Texture based digital terrain analysis methods can better explore and recognize the spatial distributing characteristics and the nested hierarchical structure characteristics of landform morphology.