Grouping Straight Line Segments in Real Images

In this paper, we discuss straight line extraction as a part of the image interpretation process. Favoring the use of line drawings as intermediate data for the extraction, we survey the current methods, which all achieve a polygonal approximation of lines, and show that they are not appropriate for the identification of straight elements in a scene. We propose a new approach which uses a scale invariant criterion and is based on the characterization of prime segments in a line, and develop an original method for obtaining these prime segments. Results show that we significantly improve the performance of straight line extraction. The methodology we have used here is applicable to a large class of segmentation problems

Validation of a forage production index (FPI) derived from MODIS fcover time-series using high-resolution satellite imagery: methodology, results and opportunities

An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. The FPI corresponds to the integral of the fraction of green vegetation cover derived from moderate spatial resolution time series images and was calculated at the 6 km x 6 km scale. An upscaled approach based on direct validation was used that compared FPI with field-collected biomass data and high spatial resolution (HR) time series images. The experimental site was located in the Lot and Aveyron departments of southwestern France. Data collected included biomass ground measurements from grassland plots at 28 farms for the years 2012, 2013 and 2014 and HR images covering the Lot department in 2013 (n = 26) and 2014 (n = 22). Direct comparison with ground-measured yield led to good accuracy (R-2 = 0.71 and RMSE = 14.5%). With indirect comparison, the relationship was still strong (R-2 ranging from 0.78 to 0.93) and informative. These results highlight the effect of disaggregation, the grassland sampling rate, and irregularity of image acquisition in the HR time series. In advance of Sentinel-2, this study provides valuable information on the strengths and weaknesses of a potential index-based insurance product from HR time series images

Detection of "Flavescence dorée" Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed

Mapping health status of chestnut forest stands using Sentinel-2 images

In many parts of France, health status of chestnut forest stands is a crucial concern for forest managers. These stands are made vulnerable by numerous diseases and sometimes unadapted forestry practices. Moreover, since last years, they were submitted to several droughts. In Dordogne province, the economic stakes are important. About 2/3 of the chestnut forest area are below the optimal production level. The actual extent of chestnut forest decline remains still unknown. Sentinel-2 time series show an interesting potential to map declining stands over a wide area and to monitor their evolutions. This study aim to propose a method to discriminate healthy chestnut forest stands from the declining ones with several levels of withering intensity over the whole Dordogne province. The proposed method is the development of a statistical model integrating in a parsimonious manner several vegetation indices and biophysical parameters. The statistical approach is based on an ordered polytomous regression to which are applied various technics of models’ selection. We aim to map 3 classes of predictive health status. In this study, Sentinel-2 images (10 bands at 10 and 20 m spatial resolution) acquired during the growing season of 2016 have been processed. Due to insufficient data quality related to atmospheric conditions, only 2 cloud-free images could be analyzed (one in July and one in September). About 36 vegetation indices were calculated from THEIA-MAJA L2A products and 5 biophysical parameters (Cover fraction of brown vegetation, Cover fraction of green vegetation, Fraction of Absorbed Photosynthetically Active Radiation, Green Leaf Area Index, Leaf water content) were processed from ESA level 1C product. These last parameters have been obtained with the Overland software (developed by Airbus DS Geo-Intelligence) by inverting a canopy reflectance model. This software couples the PROSPECT leaf model and the scattering by arbitrary inclined leaves (SAIL) canopy model. Calibration and validation of the predictive model are based on the health status of chestnut forest stands data survey. About 50 plots have been surveyed by foresters describing the chestnut trees health status by using two protocols (ARCHI and expert knowledge). Model stability over time and space will be further analyzed with Sentinel-2 time series during 2017 and 2018 on other different chestnut forest stands

Health status diagnosis of chestnut forest stands using Sentinel-2 images.

The Theia workshop for Sentinel-2 L2A MAJA products was held in Toulouse on the 13th and 14th of June 2018. About 80 people participated either on the 13th or 14th, and nearly 70 participants attended each day of this workshop, whose object was to collect feedback and share experiences on the quality, use and applications of the L2A surface reflectance products delivered by Theia from Sentinel-2 data

On the potentiality of UAV multispectral imagery to detect flavescence dorée and grapevine trunk diseases

Among grapevine diseases affecting European vineyards, Flavescence dorée (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge® sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field(proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines)

Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land

The correction of the atmospheric effects on optical satellite images is essential for quantitative and multitemporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD 90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals

Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode

International audienceFour one-dimensional radiative transfer models are compared in direct and inverse modes. These models are combinations of the PROSPECT leaf optical properties model and the SAIL, IAPI, KUUSK, and NADI canopy reflectance models. To evaluate their ability to estimate canopy biophysical parameters, inversions were first performed on synthetic reflectance spectra (10 wavelengths in the visible and near infrared). The simulated spectral and directional reflectances showed good agreement among the four models. A 1997 airborne experiment in the USA was used to test their performance on real data. This experiment gathered a unique data set composed primarily of 200 reflectance spectra acquired over corn (Zea mays L.) and soybean (Glycine max) fields, and the corresponding ground truth (chlorophyll a+b content and leaf area index). Only the first three models, which ran fast enough to allow the processing of a large data set, were actually inverted by iterative optimization techniques. Inversions were conducted in successive stages where the number of retrieved parameters was reduced. No significant difference can be observed between the three models. Globally, the leaf mesophyll structure parameter (N) and leaf dry matter content (Cm) couldn't be estimated. The chlorophyll content (Cab), the leaf area index (LAI), and the mean leaf inclination angle (θl) yielded better results, although the latter wasn't validated due to missing ground data. Assuming that model inversion by iterative optimization techniques is a promising method to extract information on plant canopies, the SAIL and KUUSK models, which perform well in terms of accuracy and running time, proved to be good candidates for remote sensing application in ecology or agriculture (precision farming)

Development of an index-based insurance product: validation of a forage production index derived from medium spatial resolution fCover time series

International audienceAn index-based insurance is being developed to estimate and monitor forage production in France in near real-time based on a forage production index (FPI) derived from the fraction of green vegetation cover (fCover) integral, obtained from medium spatial resolution time series. This article presents the first step of the scientific validation implemented. The grassland parcels, the field protocol established to collect biomass production data, and the method used to get the fCover are described. Local ground measurements of biomass production are compared with FPI values obtained from high-resolution space-based images. Discrepancies between the two variables are quantified by the coefficient of determination, the mean square error and the normalised root mean square error. First, fCover derived from the four sensors are coherent demonstrating the ability of the algorithm used to provide a consistent way of calculating fCover. Second, for the whole data set, the scatter plot between FPI and biomass shows an acceptable correlation (R-2=0.75) improved when only taking into account data recorded up until the production maximum (R-2=0.81). Third, the analysis carried out on the scale of the parcels, grass species, period of mowing or climatic conditions reveals variability on the regression coefficients indicating that other explanatory variables should be integrated to better compute the FPI