D. Panagiotidis, Azadeh Abdollahnejad, P. Surový, Karel Kuželka
{"title":"从高分辨率无人机图像中检测掉落的原木","authors":"D. Panagiotidis, Azadeh Abdollahnejad, P. Surový, Karel Kuželka","doi":"10.33494/NZJFS492019X26X","DOIUrl":null,"url":null,"abstract":"Background: High-resolution images from unmanned aerial vehicles (UAVs) can be used to describe the state of forests at regular time periods in a cost-effective manner. The purpose of this study was to assess the performance of a line template matching algorithm, the Hough transformation, for detecting fallen logs from UAV-based high-resolution RGB images. The suggested methodology does not aim to replace any known aerial method for log detection, rather it is more oriented to the detection of fallen logs in open forest stands with a high percentage of log visibility and straightness. \nMethods: This study describes a line template matching algorithm that can be used for the detection of fallen logs in an automated process. The detection technique was based on object-based image analysis, using both pixel-based and shape descriptors. To determine the actual number of fallen logs, and to compare with the ones predicted by the algorithm, manual visual assessment was used based on six high-resolution orthorectified images. To evaluate if a line matched, we used a voting scheme. The total number of detected fallen logs compared with the actual number of fallen logs based on several accuracy metrics. To evaluate predictive models we tested the cross-validation mean error. Finally, to test how close our results were to chance, we used the Cohen`s Kappa coefficient. \nResults: The detection algorithm found 136 linear objects, of which 92 of them were detected as fallen logs. From the 92 detected fallen logs, 86 were correctly predicted by the algorithm and 24 were falsely detected as fallen logs. The calculated amount of observed agreement was equal to 0.78, whereas the expected agreement by chance was 0.61. Finally, the kappa statistic was 0.44. \nConclusions: Our methodology had high reliability for detecting fallen logs based on total user‘s accuracy (94.9%), whereas a Kappa of 0.44 indicated there was good agreement between the observed and predicted values. Also, the cross-validation analysis denoted the efficiency of the proposed method with an average error of 16%.","PeriodicalId":19172,"journal":{"name":"New Zealand Journal of Forestry Science","volume":" ","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2019-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Detection of fallen logs from high-resolution UAV images\",\"authors\":\"D. Panagiotidis, Azadeh Abdollahnejad, P. Surový, Karel Kuželka\",\"doi\":\"10.33494/NZJFS492019X26X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: High-resolution images from unmanned aerial vehicles (UAVs) can be used to describe the state of forests at regular time periods in a cost-effective manner. The purpose of this study was to assess the performance of a line template matching algorithm, the Hough transformation, for detecting fallen logs from UAV-based high-resolution RGB images. The suggested methodology does not aim to replace any known aerial method for log detection, rather it is more oriented to the detection of fallen logs in open forest stands with a high percentage of log visibility and straightness. \\nMethods: This study describes a line template matching algorithm that can be used for the detection of fallen logs in an automated process. The detection technique was based on object-based image analysis, using both pixel-based and shape descriptors. To determine the actual number of fallen logs, and to compare with the ones predicted by the algorithm, manual visual assessment was used based on six high-resolution orthorectified images. To evaluate if a line matched, we used a voting scheme. The total number of detected fallen logs compared with the actual number of fallen logs based on several accuracy metrics. To evaluate predictive models we tested the cross-validation mean error. Finally, to test how close our results were to chance, we used the Cohen`s Kappa coefficient. \\nResults: The detection algorithm found 136 linear objects, of which 92 of them were detected as fallen logs. From the 92 detected fallen logs, 86 were correctly predicted by the algorithm and 24 were falsely detected as fallen logs. The calculated amount of observed agreement was equal to 0.78, whereas the expected agreement by chance was 0.61. Finally, the kappa statistic was 0.44. \\nConclusions: Our methodology had high reliability for detecting fallen logs based on total user‘s accuracy (94.9%), whereas a Kappa of 0.44 indicated there was good agreement between the observed and predicted values. 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Detection of fallen logs from high-resolution UAV images
Background: High-resolution images from unmanned aerial vehicles (UAVs) can be used to describe the state of forests at regular time periods in a cost-effective manner. The purpose of this study was to assess the performance of a line template matching algorithm, the Hough transformation, for detecting fallen logs from UAV-based high-resolution RGB images. The suggested methodology does not aim to replace any known aerial method for log detection, rather it is more oriented to the detection of fallen logs in open forest stands with a high percentage of log visibility and straightness.
Methods: This study describes a line template matching algorithm that can be used for the detection of fallen logs in an automated process. The detection technique was based on object-based image analysis, using both pixel-based and shape descriptors. To determine the actual number of fallen logs, and to compare with the ones predicted by the algorithm, manual visual assessment was used based on six high-resolution orthorectified images. To evaluate if a line matched, we used a voting scheme. The total number of detected fallen logs compared with the actual number of fallen logs based on several accuracy metrics. To evaluate predictive models we tested the cross-validation mean error. Finally, to test how close our results were to chance, we used the Cohen`s Kappa coefficient.
Results: The detection algorithm found 136 linear objects, of which 92 of them were detected as fallen logs. From the 92 detected fallen logs, 86 were correctly predicted by the algorithm and 24 were falsely detected as fallen logs. The calculated amount of observed agreement was equal to 0.78, whereas the expected agreement by chance was 0.61. Finally, the kappa statistic was 0.44.
Conclusions: Our methodology had high reliability for detecting fallen logs based on total user‘s accuracy (94.9%), whereas a Kappa of 0.44 indicated there was good agreement between the observed and predicted values. Also, the cross-validation analysis denoted the efficiency of the proposed method with an average error of 16%.
期刊介绍:
The New Zealand Journal of Forestry Science is an international journal covering the breadth of forestry science. Planted forests are a particular focus but manuscripts on a wide range of forestry topics will also be considered. The journal''s scope covers forestry species, which are those capable of reaching at least five metres in height at maturity in the place they are located, but not grown or managed primarily for fruit or nut production.