1.

INTRODUCTION

Recently, the one map policy in Indonesia began to be encouraged for large-scale maps up to 1/10.000 - 1/5.000. Hence all efforts to encourage the acceleration of large-scale map production are always needed. Using the Very High-Resolution Satellite (VHRS) imagery for mapping can be a good choice, due to its wide-area coverage. There are many kinds of researches that have used of VHRS imagery technique for producing a base map (see [1], [2], [3]). In general, The Base Map use DTM (Digital Terrain Model) as elevation information. Unfortunately, producing terrain point cloud from airborne or spaceborne with optical sensor camera is quite the challenge. The elevation point model extraction from spaceborne or aerial platform imagery only produces DSM (Digital Surface Model) instead of DTM. However, working with an aerial photo and VHRS image can produce a high spatial resolution that meets the need for large scale mapping. This study uses Worldview-3 stereo ready images that have a 30cm x 30cm spatial resolution. While the aerial photo uses 10cm Ground Sampling Distance (GSD). Furthermore, the DSM filtering process is one of the procedures that can be done for classifying terrain and non-terrain point cloud. The goal of this study is to analyze the suitability of the filtering methods towards the generation of Digital Terrain Model (DTM) from Digital Surface Model (DSM) extraction.

Some DSM filtering algorithms to classify between terrain and non-terrain point cloud has tried. There are three kind algorithm, which are: (1) Elevation Threshold with Expand Windows (ETEW), (2) 2D Morphological Square (Morph 2D Filter), and (3) Adaptive TIN (A-Tin) (see [4], [5], [6], [7]). The ALDPAT (Airborne LIDAR Data Processing and Analysis Tools) v.1.0 software was used to implement those algorithms. Although the ALDPAT program is designed for handling LIDAR data, it can be used for general point cloud data also. These filtering algorithms are implemented for residential and vegetation areas. The residential area mostly covered by houses objects. While vegetation area rather open area. The evaluation test has been made by visual interpretation to see the point remain. A good filtering result can remove a non-terrain point as much as possible. This visual interpretation can be done by using correspondence its orthoimage and orthophoto. Furthermore, an interpolation algorithm has to be done to fill the area missing point.

2.

IMAGE DATASHEET

This study uses two kinds of landscape area which are (1) Mountainous area at The Tasikmalaya District and (2) Sub-Urban area at Probolinggo District. Table 1 shows the sample image of Orthoimage from Worldview-3 and Orthophoto from aerial photo. The DSM data from Worldview-3 has derived by stereo parallax with area-based image matching from stereo epipolar image. While the DSM from the aerial photo has derived by the parallax algorithm also.

Table 1.

Image datasheet.

3.

METHODOLOGY

The methodology is arranged to fulfil the research objectives effectively (see Figure 1). The most important steps are described as follows:

Figure 1.

Workflow diagram of research

This research performs several phases of the procedure. First, acquisition of the DSM Point Cloud data from the WorldView-3 and DSM Point Cloud data from aerial photo data. Data is done filtering process using ALDPAT software in generating DTM. The DSM filtering process for both data uses the Method 3 algorithm which is ETEW, ATIN, and Morph 2D so that obtained 3 outputs from the DSM filtering process of the ALDPAT software. Once the cloud point is obtained from the DSM filtering process, the DTM resulting from the use of 3 algorithm methods, performed a visual evaluation of the data.

As for the concept explanation of the 3 algorithm methods used as follows:

(1) The three option Filtering algorithm are as follow:

ETEW: Elevation Threshold with Expand Windows (see [5], [7])

ATin: Adaptive TIN (see [6], [7])

Morph 2D Filter: 2D Morphological Square (see [4], [7])

(2) The visual interpretation has done by using its correspondent orthophoto. A terrain point cloud should be remain, while the non-terrain point should be removed. Plotting remain point cloud at its orthophoto can be used to evaluate the filtering quality.

4.

RESULT AND DISCUSSION

4.2

Final DTM

Processing DSM filtering produce bare-earth point that close to terrain point. While the non-terrain point removed. This condition make missing point in some areas. The missing point should be filled by interpolation. Figure 2 and 3 shows the comparison between DSM and DTM derived from filtering procedures in vegetation areas. It can be shown that there is still some non-terrain point at dense vegetation and residential area. This non-terrain point makes contour like bull eyes shape. The non-terrain point remaining can be detected as spike or pit in elevation value. It is need to repeat filtering procedures for removing remaining non-terrain point.

Figure 2.

Contour line at residential area

Figure 3.

Contour line at vegetation area

5.

CONCLUSION

This paper shows some algorithm for DSM filtering to remove a non-terrain point cloud. The use of different algorithms produces different remaining terrain points. The Adaptive TIN algorithm give the best result for DSM filtering procedure followed by ETEW and Morph 2D algorithm. The filtering algorithm with ATin and ETEW can working in residential and vegetation at any landscape area. While Morph 2D algorithm can be used in flat area. There are still some non-terrain point remaining after DSM filtering, because the setting parameter algorithm cannot working for varies object at the surface of the earth. In this case, the iteratively filtering process should be made. Furthermore, an interpolation process should be made for filling the missing non-terrain point.