Typical applications

MASI software can be applied to these aspects: (1) generation of mapping products (point clouds, DSM, DTM, orthorectified image, and pansharpened image); (2) automatically finding change of surface height (used in the automatic finding of new buildings, the unplanned buildings and the removed buildings, and estimating their corresponding accurate height); (3) monitoring the construction progress of new city, big residential district and huge engineering project; (4) testimony of land expropriation and housing demolition; (5) estimating the exploited volume of ores located on surface; (6) digital city and reconstruction of 3D scene; (7) collection of commercial and military intelligence; (8) accurate estimation of height of buildings and trees.

Cases and Results (The following items can be accessed by clicking):

Case 1: Generation of highly dense and accurate DSM

Case 2: Volume calculation

Case 3: Extraction of building attributes (the center position of building, height of building, number of layers, area of ground, and construction area of building)

Case 4: Results of aerial three-lines-scanner ADS (ADS40, ADS80, ADS100) images

Case 5: Results of UAV images

Case 6: Automatic DSM to DTM

Case 7: 3D reconstruction with true color textures

Case 8: Mosaicking of hyper-spectral UAV images

Case 1: Generation of highly dense and accurate DSM

Figure 1.1 Color-shaded DSM extracted from DMC images, grid size: 10 cm

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Figure 1.2 DSM extracted from frame aerial images, grid size: 10 cm

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Figure 1.3 DSM extracted from Pléiades NEO satellite images, grid size: 50 cm

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Figure 1.4 DSM extracted from Gaofen-7 (GF-7) satellite images, grid size: 1 m

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Figure 1.5 DSM extracted from aerial ADS80 (three lines scanner) images, grid size: 20 cm

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Figure 1.6 DSM extracted from UAV images, grid size: 20 cm

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Figure 1.7 DSM extracted from UAV images (vegetation in roundabout), grid size: 5 cm

Case 2: Volume calculation

Given a polygon in the vector form, a height of base plane (or DSM of previous phase) and highly dense DSM, the function estimates the volume of objects (e.g., sands, small stones, coal, mineral particles and garbage) occupying the base plane (or the surface of previous phase).

In this case, the area of the depicted boundary is 1106.00 m^2, while the volume is 3659.30 m^3.

Figure 2.1 The ortho-image automatically generated from UAV images, resolution: 5 cm

Figure 2.2 The boundary to be used to calculate volume depicted on the above ortho-image

Figure 2.3 The highly dense and accurate DSM to be used to calculate volume (hill shaded DSM), grid size: 5 cm

Figure 2.4 Overlapping displaying of the depicted boundary and the above DSM

Case 3: Extraction of building attributes (the center position of building, height of building, number of layers, area of ground, and construction area of building)

The function can be used in the automatic finding of new buildings, the unplanned buildings and the removed buildings, and estimating their corresponding accurate height, in monitoring the construction progress, in automatic finding of tall objects above a height in airport area, and in the extraction of building height.

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Figure 3.1 The building footprints collected on the height difference between two DSMs as input (building footprints can also be from third party source)

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Figure 3.2 Given a vector file with ERSI shape format shown in figure 3.1, which including multiple polygons of building footprint, and the corresponding nDSM dataset (i.e., the height difference, which can be generated from the module “Surface Change” in the main interface), it automatically extracts the center position (x, y coordinates) of building, area of ground, height of building, number of layers, and construction area of building for each building. Moreover, these extracted values are set as new attributes for these polygons.

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Figure 3.3 The building attributes (the center position of building, height of building, number of layers, area of ground, and construction area of building) automatically extracted from SuperView-1 stereo images. Building footprints can be from third party source or be collected by using MASI software. These extracted attributes are automatically added to the vector file. The buildings are visualized via GIS platform in the figure.

Case 4: Results of aerial three-lines-scanner ADS (ADS40, ADS80, ADS100) images

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Figure 4.1 Pixel-wise DSM extracted from aerial ADS images, grid size: 20 cm

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Figure 4.2 Pixel-wise DSM extracted from aerial ADS images, grid size: 20 cm

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Figure 4.3 Pixel-wise DSM extracted from aerial ADS images, grid size: 20 cm

Case 5: Results of UAV images

Figure 5.1 The generated point clouds, trees, grid size: 5 cm

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Figure 5.2 The highly dense and accurate DSM generated, top of building, grid size: 5 cm

Figure 5.3 The generated DSM before interpolation, the buildings under construction and cranes, grid size: 20 cm

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Figure 5.4 The true ortho-mosaicked image automatically generated from UAV images (flowchart: the generation of highly dense DSM -> ortho rectification using the DSM -> mosaicking). For the sake of showing the seaming effect, the adjacent images with color difference are selected.

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Figure 5.5 The true ortho-mosaicked image automatically generated from UAV images (flowchart: the generation of highly dense DSM -> ortho rectification using the DSM -> mosaicking). For the sake of showing the seaming effect, the adjacent images with color difference are selected.

Case 6: Automatic DSM to DTM

DSM DTM

Figure 6.1 DTM transformed from DSM extracted from satellite image with 1 meter grid size

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(a) DSM

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(b) DTM

Figure 6.2 (a) DSM extracted from SWDC-4 aerial images by MASI software, grid size: 20 cm; (b) DTM transformed from DSM, grid size: 20 cm

Case 7: 3D reconstruction with true color textures

Figure 7.1 3D TIN models with true color textures. The above three figures are generated from Pléiades 70 cm triplet images.

Figure 7.2 3D TIN models with true color textures. The figure is generated from Pléiades NEO 30 cm triplet images.

Case 8: Mosaicking of hyper-spectral UAV images

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Figure 8.1 The mosaicked results of hyper-spectral UAV images, 176 bands, the ground slope is high.