... | @@ -37,6 +37,9 @@ The morning session will be dedicated to short talks from users and contributors |
... | @@ -37,6 +37,9 @@ The morning session will be dedicated to short talks from users and contributors |
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* SAR Sentinel-1 images pre-processing : a processing chain in Python, based upon OTB.
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* SAR Sentinel-1 images pre-processing : a processing chain in Python, based upon OTB.
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* Integration of OTB in remote platform
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* Integration of OTB in remote platform
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* **[CONFIRMED]** Testing OTB's Segmentation methods (Agustin Lobo - ICTJA-CSIC)
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* **[CONFIRMED]** Testing OTB's Segmentation methods (Agustin Lobo - ICTJA-CSIC)
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> Image segmentation partitions the image into patches that are internally consistent in terms of image properties. This is an important step in those (most common) cases in which landscape units of interest are configured by many non-uniform pixels and thus single-pixel properties are not sufficient to characterize a unit type. Segmentation becomes increasingly important as remotely sensed imagery becomes of increasing spatial resolution. In this presentation, after a brief introduction, I will focus on practical aspects of segmentation with OTB such as the impact of varying the different parameters involved in Mean-Shift based segmentation.
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* **[CONFIRMED]** Automatic segmentation of Loire habitats: Example of the islands of the Mareaux-aux-Prés National Nature Reserve (Martin Hilaire - IRSTEA)
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* **[CONFIRMED]** Automatic segmentation of Loire habitats: Example of the islands of the Mareaux-aux-Prés National Nature Reserve (Martin Hilaire - IRSTEA)
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> Monitoring vegetation dynamics in nature reserves requires accurate mapping of habitats. In August 2017, we used a drone to acquire LAS images and high-resolution (2cm)orthophotos on islands of nature reserve of St-Mesmin along the Loire river. At the same time, we mapped habitats based on vegetation sampling every 15 m and the phytosociological nomenclature published by the Botanical Conservatory of the Paris Basin. The OTB workflow allowed us to map 9 vegetation types but with an overall kappa of 0.60 only. If the results were satisfactory for the different riparian forest types (min F-score = 0.70) , the various tools tested under OTB failed to correctly classify open areas as reed-bed (F-score = 0.31) and grassland (F-score = 0.18). Two hypotheses can explain this problem: the low accuracy of the field GPS data and some subjectivity in the interpretation of the phytosociological nomenclature.
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> Monitoring vegetation dynamics in nature reserves requires accurate mapping of habitats. In August 2017, we used a drone to acquire LAS images and high-resolution (2cm)orthophotos on islands of nature reserve of St-Mesmin along the Loire river. At the same time, we mapped habitats based on vegetation sampling every 15 m and the phytosociological nomenclature published by the Botanical Conservatory of the Paris Basin. The OTB workflow allowed us to map 9 vegetation types but with an overall kappa of 0.60 only. If the results were satisfactory for the different riparian forest types (min F-score = 0.70) , the various tools tested under OTB failed to correctly classify open areas as reed-bed (F-score = 0.31) and grassland (F-score = 0.18). Two hypotheses can explain this problem: the low accuracy of the field GPS data and some subjectivity in the interpretation of the phytosociological nomenclature.
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