Comparison of the strip- and block-wise aerial triangulation using different exterior orientation parameters weights

Figures & data

Figure 1. Left: land cover of the test field with images perspective centres, ground control points and check point’s location. Right: the location of the photogrammetric block (black configuration) in Sweden.

Figure 2. Systematic shift and linear drift errors, modified after Trimble (2015).

Table 1. The number of rejected images and checkpoint RMS of some best case, worst case and Lantmäteriet default (*) values for observable uncertainties.

Observable uncertainty
GNSS (metre)	IMU ($\text{^^circ}$)	Numbers of rejected image							RMS Residual check Points (m)
u(E), u(N), u(H)	u(ω), u(φ), u($\kappa$)	E	N	H	ω	φ	$\mathit{\kappa }$	Total
0.2, 0.2, 0.2	0.007, 0.007, 0.009	0	1	0	0	0	0	1	0.157
	0.006, 0.006, 0.008	0	1	0	1	0	0	2	0.158
	0.008, 0.008, 0.002	0	1	0	0	0	537	538	0.151
	0.003, 0.003, 0.007	0	0	0	92	0	3	95	0.168	*
	0.001, 0.001, 0.001	36	33	0	1188	2	748	2007	0.184
	0.001, 0.001, 0.009	1	0	0	1193	1	0	1195	0.192
0.08, 0.08, 0.08	0.003, 0.003, 0.007	1	6	0	65	0	3	75	0.157
0.04, 0.04, 0.04		0	3	14	60	0	3	80	0.157
0.12, 0.12, 0.12		1	5	0	76	0	3	85	0.16
0.2, 0.2, 0.2		0	0	0	92	0	3	95	0.168	*
0.36, 0.36,0.2		0	0	0	100	0	3	103	0.177
0.36, 0.36, 0.36		0	0	0	102	0	3	105	0.181
0.08, 0.08, 0.08	0.007, 0.007, 0.009	0	7	1	0	0	0	8	0.154

Figure 3. The minimum number of rejected images when using 0.007$\text{^^circ}$, 0.007$\text{^^circ}$ and 0.009$\text{^^circ}$uncertainties for $\omega ,\phi ,\kappa$ also 0.2 m uncertainties for $E,N,H$, respectively.

Figure 4. The maximum number of rejected images when using 0.001$\text{^^circ}$, 0.001$\text{^^circ}$ and 0.001$\text{^^circ}$uncertainties for $\omega ,\phi ,\kappa$ also 0.2 m uncertainties for $E,N,H$, respectively (all images rejected by $\omega$ but it is not shown in this figure).

Figure 5. Comparison of checkpoints’ vertical residual vectors for case 1 (red vectors) and case 2 (blue vectors) on land cover. Blue vectors have an offset from their initial location due to a better display.

Table 2. Statistical analysis for elevation differences between LiDAR’s DTM and Photogrammetry DSMs.

GNSS (m)	IMU(${ }^{\circ }$)	Area	Statistical parameters (m)
u(E), u(N), u(H)	u(ω), u(φ), u(κ)		Mean	RMS of residual	Variance
0.2, 0.2, 0.2	0.003, 0.003, 0.007	Road	0.095	1.877	3.522
0.2, 0.2, 0.2	0.007, 0.007, 0.009		0.086	1.832	3.358
0.08, 0.08, 0.08	0.003, 0.003, 0.007		0.053	1.88	3.534
0.08, 0.08, 0.08	0.007, 0.007, 0.009		0.054	1.854	3.438

Trimble, 2015. MATCH-AT reference manual. Germany: Trimble inpho.

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