Extracting Terrain Texture Features for Landform Classification Using Wavelet Decomposition

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).

Quantification of Surface Pattern Based on the Binary Terrain Structure in Mountainous Areas

“…These objects provide the potential units to calculate indices and analyze the spatial characteristics of surface patterns. The extraction and classification of topographic units have supported the studies of landform spatial variation [19,23], surface textural characteristics [58,59] and landform development [31].…”

Crop Classification and Growth Monitoring in Coal Mining Subsidence Water Areas Based on Sentinel Satellite

“…The classification impact will be influenced by the phenomena of "same thing with different spectrum and foreign item with the same spectrum", even if the spectral index may distinguish vegetation, water bodies, buildings, etc. Researchers [31,32] have attempted to calculate texture features to supplement the spectral information to improve classification accuracy and better distinguish between different ground objects. Therefore, this study selected six commonly used texture indices (Contrast, Variance, Entropy, Inverse Difference Moment, Angular Second Moment, and Correlation) to be included as texture features in the classification.…”