Extracting Terrain Texture Features for Landform Classification Using Wavelet Decomposition
Bibliographic Data
| ID | 22034447 |
|---|---|
| Authors | Yuexue Xu (0000-0002-2200-2280, Shandong University of Science and Technology), Shengjia Zhang (0009-0004-0209-2276, Shandong University of Science and Technology), Jinyu Li (0009-0004-9733-4476, Shandong University of Science and Technology), Haiying Liu (0000-0001-9431-3419, Shandong University of Science and Technology, corresponding author), Hongchun Zhu (0000-0002-3506-0017, Shandong University of Science and Technology) |
| Year | 2021 |
| Volume | 10 |
| Issue | 10 |
| Pages | 658 |
| Publication date | 2021-09-30 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10100658 |
| OpenAlex | W3202811029 |
| Language | EN |
| Citations received | 1 |
| References cited | 48 |
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)
Cartography · Computer vision · Discrete wavelet transform · Geography · Geomorphology · Landform · Principal component analysis · Random forest · Remote sensing · Terrain · Wavelet · Wavelet transform · Computer Science · Image Processing and 3D Reconstruction · Landslides and related hazards · Soil Geostatistics and Mapping · Artificial Intelligence · Geology
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |