|Appears in Collections:||Biological and Environmental Sciences Conference Papers and Proceedings|
Dos Santos, Jefersson A
Borges, Bruno D
Silva, Thiago S F
Morellato, Leonor Patricia
Torres, Ricardo da S
|Title:||Semantic segmentation of vegetation images acquired by unmanned aerial vehicles using an ensemble of ConvNets|
|Citation:||Nogueira K, Dos Santos JA, Cancian L, Borges BD, Silva TSF, Morellato LP & Torres RdS (2017) Semantic segmentation of vegetation images acquired by unmanned aerial vehicles using an ensemble of ConvNets. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium Proceedings. 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA, 23.07.2017-28.07.2017. Piscataway, NJ, USA: IEEE, pp. 3787-3790. https://doi.org/10.1109/IGARSS.2017.8127824|
|Series/Report no.:||IEEE International Geoscience and Remote Sensing Symposium Proceedings|
|Conference Name:||2017 IEEE International Geoscience and Remote Sensing Symposium|
|Conference Dates:||2017-07-23 - 2017-07-28|
|Conference Location:||Fort Worth, TX, USA|
|Abstract:||Vegetation segmentation in high resolution images acquired by unmanned aerial vehicles (UAVs) is a challenging task that requires methods capable of learning high-level features while dealing with fine-grained data. In this paper, we propose a combination of different methods of semantic segmentation based on Convolutional Networks (ConvNets) to obtain highly accurate segmentation of individuals of different vegetation species. The objective is not only to learn specific and adaptable features depending on the data, but also to learn and combine appropriate classifiers. We conducted a systematic evaluation using a high-resolution UAV-based image dataset related to a campo rupestre vegetation in the Brazilian Cerrado biome. Experimental results show that the ensemble technique overcomes all segmentation strategies.|
|Status:||VoR - Version of Record|
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