Orfeo ToolBox: open source processing of remote sensing images

Grizonnet, Manuel; Michel, Julien; Poughon, Victor; Inglada, Jordi; Savinaud, Mickaël; Cresson, Romain

doi:10.1186/s40965-017-0031-6

Cited by 206 publications

(122 citation statements)

References 15 publications

(14 reference statements)

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“…All bands were scaled from raw image digital numbers (11 bits) to 0-255 (8 bits). The red-green-blue (RGB) bands were pansharpened with the panchromatic band using the method Simple RCS of the Orfeo toolbox addon otbcli_ BundleToPerfectSensor (Grizonnet et al 2017) to create a single high resolution RGB image at a spatial resolution of 0.3 m. We used only RGB bands as the targets were already identifiable with a high confidence in the pan-sharpened RGB image and also for the parsimony of the model. No atmospheric correction was performed.…”

Section: Worldview-3 Images and Preprocessingmentioning

confidence: 99%

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Wagner

Sánchez

Tarabalka³

et al. 2019

Remote Sens Ecol Conserv

148

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Mapping forest types and tree species at regional scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we assess the potential of a U‐net convolutional network, a recent deep learning algorithm, to identify and segment (1) natural forests and eucalyptus plantations, and (2) an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView‐3 satellite in the Brazilian Atlantic rainforest region. The networks for forest types and Cecropia trees were trained with 7611 and 1568 red‐green‐blue (RGB) images, respectively, and their dense labeled masks. Eighty per cent of the images were used for training and 20% for validation. The U‐net network segmented forest types with an overall accuracy >95% and an intersection over union (IoU) of 0.96. For C. hololeuca, the overall accuracy was 97% and the IoU was 0.86. The predictions were produced over a 1600 km2 region using WorldView‐3 RGB bands pan‐sharpened at 0.3 m. Natural and eucalyptus forests compose 79 and 21% of the region's total forest cover (82 250 ha). Cecropia crowns covered 1% of the natural forest canopy. An index to describe the level of disturbance of the natural forest fragments based on the spatial distribution of Cecropia trees was developed. Our work demonstrates how a deep learning algorithm can support applications such as vegetation, tree species distributions and disturbance mapping on a regional scale.

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Section: Worldview-3 Images and Preprocessingmentioning

confidence: 99%

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Wagner

Sánchez

Tarabalka³

et al. 2019

Remote Sens Ecol Conserv

148

View full text Add to dashboard Cite

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“…The algorithm provides a scene classification including pixels affected by noise such as clouds and cloud shadows. Those pixels were masked and filled using a temporal gap‐filling method ( Image Time Series Gap Filling application from the Orfeo ToolBox (version 6.6); OTB (Grizonnet et al, ; OTB Development Team, )) that replaces the masked/invalid pixels with a value interpolated from the valid dates of the time series. During this process, the time series of each tile were also resampled to the same temporal grid to facilitate the subsequent mosaicking.…”

Section: Methodsmentioning

confidence: 99%

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

et al. 2020

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The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel‐1 and Sentinel‐2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel‐1, +2.5% for Sentinel‐2 and +2% combining Sentinel‐1 and Sentinel‐2). Overall, combining Sentinel‐2 and Sentinel‐1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open‐source tools satellite imagery.

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“…Spectral Python is an open-source Python library that has several image processing, clustering, and classification algorithms built-in [3]. The Orfeo Toolbox [12] and RSGISLib [5] are both open-source libraries that specialize in Geographic Object Base Image Analysis (GEOBIA) based classification. They both have some advanced feature extraction methods that can be used to improve performance.…”

Section: Existing Hsi Analysis Tool Boxesmentioning

confidence: 99%

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

Kemker

Luu

Kanan

2018

IEEE Trans. Geosci. Remote Sensing

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Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success. Since generating sensor specific datasets is time intensive and cost prohibitive, remote sensing researchers have embraced deep unsupervised feature extraction. Although these methods have pushed state-of-the-art performance on current HSI benchmarks, many of these tools are not readily accessible to many researchers. In this letter, we introduce a software pipeline, which we call EarthMapper, for the semantic segmentation of non-RGB remote sensing imagery. It includes self-taught spatial-spectral feature extraction, various standard and deep learning classifiers, and undirected graphical models for post-processing. We evaluated EarthMapper on the Indian Pines and Pavia University datasets and have released this code for public use.

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Orfeo ToolBox: open source processing of remote sensing images

Cited by 206 publications

References 15 publications

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

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