1. Micro-terrain feature identification and processing: An overview with practical implementations along with discussion of a potential application area of wavelet analysis Syed Amer Mehmood, Syed Roshaan, Anees Haider

2. Concavity and Convexity Areas bumped up (termed convex) and others pushed down (termed concave). On a 90m by 90m micro-level features relate to the local topographic character. Major role in the idea of variable rate response and feasibility of spatial activities, such as logging, mining, road building and housing development. The world for computer is digital, how can it see the bumps? Calculation of a difference surface is simply the start of micro-terrain analysis.

3. Basic Premise: The purpose of the analysis is to determine the site of the change (position), the type of change, and the amplitude of the change. The local aspects of wavelet analysis are well adapted for processing this type of event, as the processing scales are linked to the speed of the change. So, there is a possibility of applying wavelets in micro-terrain feature identification.

4. Introduction and review: Quite a bit of work done in applications of wavelet analysis in the context of Image processing, not as much in terrain processing. However, a detailed and targeted study relevant to these two fields is a need to further developments. Following is a brief review of some of the works related to these concepts and their significance.

5. Introduction and review: ‘Wavelets and their applications’ by Georges Oppenheim A wavelet is a function oscillating as a wave but quickly damped. – Being well localized simultaneously in time and frequency it makes it possible to define a family of analyzing functions by translation in time and dilation in scale. Wavelets constitute a mathematical “zoom” making it possible to simultaneously describe the properties of a signal on several timescales. Denoising or estimation of functions. More powerful and easy to tune than the traditional methods. Wavelets used to detect seismic jolts from the data, to detect discontinuities, and abrupt changes. This can lead to a detection of faults and abrupt terrain variations using wavelets on the profile variations obtained from the terrain.

6. Procedures and results/discussions: The principle consists of calculating the wavelet transform of observations, then astutely modifying the coefficients profiting from their local nature and, finally, using the suitably selected residuals The procedure is applied on a DEM from Mansehra and Abbottabad district area which is shown below.

7. Procedures and results/discussions: The 3d view of the site of study is given below

8. Procedures and results/discussions: Landsat image draped over DEM

9. Procedures and results/discussions: The hydrologic setup of the area

10. Procedures and results/discussions: One of the most frequently used procedures involves comparing the trend of the surface to the actual elevation values. Locations above the trend line identify convex features while locations below identify the concave ones. The further above or below determines how pronounced the feature is. In a GIS, simple smoothing of the actual elevation values derives the trend of the surface.

11. Procedures and results/discussions:

12. 7*7 smooth difference

13. Procedures and results/discussions:

14. Deviation and Coefficient of Variation filters to an elevation surface.

15. Using wavelets in profile analysis:

16. The purpose is to show how analysis by wavelets can detect the exact instant when a signal changes. The first- and second-level details (D1 and D2) show the discontinuity most clearly, because the rupture contains the high-frequency part. The main characteristic of these phenomena is that the change is localized in time or in space.

17. Using wavelets in profile analysis:

18. Applying on DEM

19. Applying Thresholds on coefficients

20. The residuals of thresholding

21. Concave and Convex from residuals

22. Comparison Taditionalvswavelet residuals

23. Comparison

24. Similarities visually evident. But along with more fluctuations, there are smaller level details as well in wavelet method. Wavelet Traditional Trend surface

25. Further points Two areas in which further enhancement to validate – Using different types of wavelets and selecting the most suitable types for these methods. For this study found ‘dmey’ to be visually better but need quantitative results – Development of a thresholding mechanism for the residuals to suit for extracting concave and convex portions

26. Thanks