Cabezas, M.; Kentsch, S.; Tomhave, L.; Gross, J.; Caceres, M.L.L. et al.: Detection of invasive species in Wetlands: Practical dl with heavily imbalanced data. In: Remote Sensing 12 (2020), Nr. 20, 3431. DOI: https://doi.org/10.3390/rs12203431
Abstract: | |
Deep Learning (DL) has become popular due to its ease of use and accuracy, with Transfer Learning (TL) effectively reducing the number of images needed to solve environmental problems. However, this approach has some limitations which we set out to explore: Our goal is to detect the presence of an invasive blueberry species in aerial images of wetlands. This is a key problem in ecosystem protection which is also challenging in terms of DL due to the severe imbalance present in the data. Results for the ResNet50 network show a high classification accuracy while largely ignoring the blueberry class, rendering these results of limited practical interest to detect that specific class. Moreover, by using loss function weighting and data augmentation results more akin to our practical application, our goals can be obtained. Our experiments regarding TL show that ImageNet weights do not produce satisfactory results when only the final layer of the network is trained. Furthermore, only minor gains are obtained compared with random weights when the whole network is retrained. Finally, in a study of state-of-the-art DL architectures best results were obtained by the ResNeXt architecture with 93.75 True Positive Rate and 98.11 accuracy for the Blueberry class with ResNet50, Densenet, and wideResNet obtaining close results. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | |
License of this version: | CC BY 4.0 Unported |
Document Type: | Article |
Publishing status: | publishedVersion |
Issue Date: | 2020 |
Appears in Collections: | Naturwissenschaftliche Fakultät |
pos. | country | downloads | ||
---|---|---|---|---|
total | perc. | |||
1 | Germany | 19 | 40.43% | |
2 | United States | 5 | 10.64% | |
3 | China | 5 | 10.64% | |
4 | Zambia | 4 | 8.51% | |
5 | France | 3 | 6.38% | |
6 | Netherlands | 2 | 4.26% | |
7 | Indonesia | 2 | 4.26% | |
8 | Czech Republic | 2 | 4.26% | |
9 | Taiwan | 1 | 2.13% | |
10 | Russian Federation | 1 | 2.13% | |
other countries | 3 | 6.38% |
Hinweis
Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.