Analysis of large-scale UAV images using a multi-scale hierarchical representation

Figures & data

Figure 1. A brief schematic diagram for analysis of large-scale UAV images. Source: Li, Tang et al. (2015).

Table

Input: //x is superpixels

Output the root node of BPT.

//I − large scale UAV image, N − the number of superixels

//class node: value, left, right, parent, adjacents;

Initialize: rag ← < node1, node2 >

RAGS ← priority queue < vector < rag > , cmprag >

i = 1

Repeat: while i < N

for each xi ∊ I

Update nodei.adjacents

for nodea ∊ nodei.adjacents

rag ← < nodei, nodea >

RAGS.push(rag)

end for

end for

end while

cycling merge:

while

newnode ← MERGE(rag)

for nodea ∊ newnode.adjacents

rag ← < newnode, nodea >

RAGS.push(rag)

end for

end while

operation cmprag(rag1, rag2)

for each rag

< node1, node2 > ← rag

return rag1.dist > rag2.dist

end operation

function newnode = MERGE(rag)

newnode.value ← (node1.value + node2.value)/2

newnode.left ← node1, node1.parent ← newnode

newnode.right ← node2, node2.parent = newnode

newnode.adjacents ← node1.adjacents ∪ node2.adjacents − node1 − node2

end function

Figure 2. The hierarchical representation of a UAV image in different levels. (a) A UAV image; (b) Several levels representation of the image.

Figure 3. All valid tree slices of a particular BPT.

Figure 4. BPT and the corresponding path matrix. (a) BPT. (b) Path matrix.

Figure 5. The same kind of node searching strategy.

Figure 6. The large-scale UAV images of the 2013 Ya’an earthquake. Source: Li, Tang et al. (2015).

Figure 7. The experimental large-scale UAV image of Munich. Source: Koch et al. (2016).

Figure 8. The optimal segmentation result of UAV image in the 2013 Ya’an earthquake. Source: Li, Tang et al. (2015).

Figure 9. The intensity average of objects of UAV image in the 2013 Ya’an earthquake. Source: Li, Tang et al. (2015).

Figure 10. The optimal segmentation result of large-scale UAV image of Munich. (a) Segmentation result with red edges. (b) Segmentation result with intensity average. Source: Koch et al. (2016).

Table 1. The efficiency analysis for the two large-scale UAV images.

UAV image	Number of superpixels	DP_Time (s)	UES (min)
Ya’an	4508	0.650	18.450
Munich	13,390	1.915	49.356

Figure 11. The distinguished area of grassy land and buildings in the segmentation results. (a) The distinguished area one in Munich. (Source: Li, Tang et al. 2015) (b) The distinguished area two in Ya’an. (Source: Koch et al. 2016)

Figure 12. The road extraction result with red edges in Ya’an UAV image. Source: Li, Tang et al. (2015).

Figure 13. The road extraction result with intensity average in Ya’an UAV image. Source: Li, Tang et al. (2015).

Figure 14. Road extraction results of UAV images in Munich. (a) Extraction result with red edge. (b) Extraction result with intensity average. Source: Koch et al. (2016).

Li, S., H. Tang, S. He, Y. Shu, T. Mao, J. Li, and Z. Xu. 2015. “Unsupervised Detection of Earthquake-Triggered Roof-Holes From UAV Images Using Joint Color and Shape Features.” IEEE Geoscience and Remote Sensing Letters 12: 1823–1827.

 Web of Science ®Google Scholar

Koch, T., P. D’Angelo, F. Kurz, F. Fraundorfer, P. Reinartz, and M. Körner. 2016. “The TUM-DLR Multimodal Earth Observation Evaluation Benchmark.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop on Geo-Spatial Computer Vision, Las Vegas, USA, June 26–July 1: 19–26.

 Google Scholar