Abstract
Accurate landform classification is a crucial component of geomorphology. Although extensive classification efforts have been exerted based on the terrain factor, the scale analysis to describe the macro and micro landform features still needs standard measurement. To obtain the appropriate analysis scale of landform structure feature, and then carry out landform classification using the terrain texture, the texture feature is introduced for reflecting landform spatial differentiation and homogeneity. First, applying the ALOS World 3D-30m (AW3D30) DEM and selecting typical landforms of the southwest Tibet Plateau, the discrete wavelet transform (DWT), which acts as the texture feature analysis method, is executed to dissect the multiscale structural features of the terrain texture. Second, through the structural indices of reconstructed texture images, the optimum decomposition scale of DWT is confirmed. Under these circumstances, wavelet coefficients and wavelet energy entropy are extracted as texture features. Finally, the random forest (RF) method is utilized to classify the landform. Results indicate that the texture feature of DWT can achieve higher classification accuracy, which increases by approximately 11.8% compared with the gray co-occurrence matrix (GLCM).
Highlights
The parameters of the decomposition scales and vectors of wavelet texture features are obtained at a later time
The digital elevation model (DEM) texture image is decomposed layer by layer according to the theory of discrete wavelet transform (DWT)
To ensure the relatively uniform selection of samples for each landform type, the sample size is selected as 256 × 256 pixels. Considering that it is a discrete dyadic wavelet transform (DDWT), the zero-padding method was used to deal with obvious splicing boundaries
Summary
The classification principle should classify the landform types and embody the diversity of spatial structure, and explain the formation mechanisms of landforms, which suffer from external forces in temporal and spatial sequence [2]. The geo-informatics graphic methodology has become a major analysis method, including the slope spectrum analysis, profile spectrum analysis, and terrain texture analysis [3,4,5]. Plentiful studies on landform classification, which is in accordance with slope spectrum features [6,7] and profile curvature [8,9], have been conducted. The landform structure is inconvenient to grasp at different scales and lacks scale universality among different study areas. As one of the elements of landform surface, has formed unique landform features [10,11]
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