Generating impact maps from bomb craters automatically detected in aerial wartime images using marked point processes

A Multi-Feature Fusion-Based Method for Crater Extraction of Airport Runways in Remote-Sensing Images

“…At the same time, the overall quality of the proposed crater extraction method in this paper is relatively high, as shown in Table 4. In contrast, the detection results of the crater detection algorithm based on marker point processing [32] are not ideal. The detection results of the method [32] include a large number of false targets in the background, such as shadows, and also miss a large number of crater targets located in the runway, as shown in Figures 10d and 11d.…”

“…To verify the effectiveness of the proposed crater extraction method, the result obtained by the crater detection algorithm based on marked point processing and the ground truth data of craters inside the runway obtained by manual annotation are also presented in Figures 10 and 11. The quantitative results of the proposed crater extraction method and the method [32] are given in Tables 4 and 5, respectively. As can be seen in Figures 10 and 11, the experimental results of the proposed method show that the runway extraction results and crater extraction results are consistent with the real situation.…”

“…On the other hand, research related to crater extraction from remote-sensing images mainly focuses on two fields: unexploded ordnances estimation [27][28][29][30][31][32][33][34] and meteor crater detection [35][36][37][38][39][40][41][42][43][44]. The methods used in the former field mainly include supervised learning methods [27][28][29][30][31] and active detection methods based on marked point processing [32,33], circular features [34], etc.…”

“…On the other hand, research related to crater extraction from remote-sensing images mainly focuses on two fields: unexploded ordnances estimation [27][28][29][30][31][32][33][34] and meteor crater detection [35][36][37][38][39][40][41][42][43][44]. The methods used in the former field mainly include supervised learning methods [27][28][29][30][31] and active detection methods based on marked point processing [32,33], circular features [34], etc. The methods used in the field of meteor crater detection are divided into two categories: based on the basic characteristics of craters such as circular features [35][36][37], shadow-illuminated region features [38,39], etc.…”

“…Generally, the feature-based active detection methods rely more on the shape characteristics of craters. For example, the author [32] used circles to represent the crater model, which was then embedded in the framework of marked point processes. By means of stochastic sampling, the most likely configuration of craters is determined.…”