Optimizing algorithms for thresholding segmentation applied to weed detection on UAV remote images.

En este trabajo se ha buscado la implementación de una alternativa al método de Otsu (1979) desarrollada por Hui-Fuang Ng (2006), el cual maximiza la diferencia entre varianzas espectrales y realiza una búsqueda multiumbral. En el estudio se emplearon imágenes procedentes de vehículos aéreos no tripulados (UAVs) tomadas en cultivos de maíz y girasol. Con una única ejecución del algoritmo en un entorno de análisis orientado a objetos, se discriminan aquellos objetos correspondientes a la fracción vegetal del suelo desnudo y se estima un umbral diferenciador entre cultivo y malas hierbas que contribuya a un subsiguiente proceso de clasificación. La técnica de Hui-Fuang detectó un mayor porcentaje de vegetación en todos los casos estudiados, oscilando el incremento entre un 3% y un 20%.This works aimed to implement an alternative to Otsu’s method (1979) developed by Hui- Fuang Ng (2006), which maximizes the difference between spectral variances and performs a multithreshold seeking. Unmanned aerial images taken in maize and sunflower crops were used in the research. In a single algorithm execution applied to an Object Based Image Analysis environment, the objects corresponding to both the vegetation fraction and bare soil are discriminated and a threshold to separate crop from weeds was also estimated, making easier a subsequent classification process. Fui-Huang’s technique provides a higher percentage of vegetation detection in all the cases, with an improvement which ranges from 3% to 20%

Measurements of the νμ\nu_{\mu} and νˉμ\bar{\nu}_{\mu}-induced Coherent Charged Pion Production Cross Sections on 12C^{12}C by the T2K experiment

We report an updated measurement of the $\nu_{\mu}$-induced, and the first measurement of the $\bar{\nu}_{\mu}$-induced coherent charged pion production cross section on $^{12}C$ nuclei in the T2K experiment. This is measured in a restricted region of the final-state phase space for which $p_{\mu,\pi} > 0.2$ GeV, $\cos(\theta_{\mu}) > 0.8$ and $\cos(\theta_{\pi}) > 0.6$, and at a mean (anti)neutrino energy of 0.85 GeV using the T2K near detector. The measured $\nu_{\mu}$ CC coherent pion production flux-averaged cross section on $^{12}C$ is $(2.98 \pm 0.37 (stat.) \pm 0.31 (syst.) \substack{ +0.49 \\ -0.00 } \mathrm{ (Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The new measurement of the $\bar{\nu}_{\mu}$-induced cross section on $^{12}{C}$ is $(3.05 \pm 0.71 (stat.) \pm 0.39 (syst.) \substack{ +0.74 \\ -0.00 } \mathrm{(Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The results are compatible with both the NEUT 5.4.0 Berger-Sehgal (2009) and GENIE 2.8.0 Rein-Sehgal (2007) model predictions

Measurements of the νμ and ν¯μ -induced coherent charged pion production cross sections on C12 by the T2K experiment

We report an updated measurement of the ν μ -induced, and the first measurement of the ¯ ν μ -induced coherent charged pion production cross section on 12 C nuclei in the Tokai-to-Kamioka experiment. This is measured in a restricted region of the final-state phase space for which p μ , π > 0.2     GeV , cos ( θ μ ) > 0.8 and cos ( θ π ) > 0.6 , and at a mean (anti)neutrino energy of 0.85 GeV using the T2K near detector. The measured ν μ charged current coherent pion production flux-averaged cross section on 12 C is ( 2.98 ± 0.37 ( stat ) ± 0.31 ( syst ) + 0.49 − 0.00 ( Q 2   model ) ) × 10 − 40     cm 2 . The new measurement of the ¯ ν μ -induced cross section on 12 C is ( 3.05 ± 0.71 ( stat ) ± 0.39 ( syst ) + 0.74 − 0.00 ( Q 2   model ) ) × 10 − 40     cm 2 . The results are compatible with both the NEUT 5.4.0 Berger-Sehgal (2009) and GENIE 2.8.0 Rein-Sehgal (2007) model predictions

View of a spectral bands and vegetation index (SBVI) file.

<p>This was made by the CROPCLASS-2.0 software (phase I) for each parcel of each multitemporal image (T1 to T7).</p

Six selected independent variables for the diverse DT training analysis studies (in parentheses % of normalized importance).

<p>^<b>α</b>Image taking time from early April (T1) to October (T7) at about one month interval</p><p>^<b>β</b>Abbreviations: Veg, vegetation; Non-Veg, non-vegetation; CropSys, Cropping systems; ATO, adult tree orchard; SUC, summer crops; WIC, winter crops; vegetation indices: NDVI, Stu (R/G) and B/G. and spectral bands: B, G, R and NIR.</p><p>Six selected independent variables for the diverse DT training analysis studies (in parentheses % of normalized importance).</p

Percentage of correctly classified parcels for all of the crops/land uses as affected by the number and timing of the image.

<p>^<b>α</b>Image taking time from early April (T1) to October (T7) at about one month interval</p><p>^<b>β</b>Abbreviations: BNS, broad beans; CKP, chickpeas; CIT, citrus orchards; COT, cotton; CRN, corn; MFO, Mediterranean forest; OAT, oat; OLV, olive orchards; POP,</p><p>Poplars grove; POT, potatoes; SUC, summer crops; SUN, sunflower; WHT, winter wheat; WIC, winter crops;</p><p>^<b>$</b>OA overall accuracy;</p><p>^<b>٭</b>Tree risk, estimated±standard error;</p><p>Percentage of correctly classified parcels for all of the crops/land uses as affected by the number and timing of the image.</p

Phenology features in Southern Spain for the crops that were considered in this study.

<p>Phenology features in Southern Spain for the crops that were considered in this study.</p

Confusion matrix for the classification of unidentified adult tree orchard (ATO), summer crop (SUC) and winter crop (WIC) parcels by implementing SQL predictive models.

<p>^<b>α</b>Abbreviations: BNS, broad beans; CKP, chickpeas; CIT, citrus orchards; COT, cotton; CRN, corn; MFO, Mediterranean forest; OAT, oat; OLV, olive orchards; POP, poplars grove; POT, potatoes; SUC, summer crops; SUN, sunflower; WHT, winter wheat;</p><p>^βUA, User accuracy; PA: Producer Accuracy; OA, overall accuracy.</p><p>Confusion matrix for the classification of unidentified adult tree orchard (ATO), summer crop (SUC) and winter crop (WIC) parcels by implementing SQL predictive models.</p

Partial view of the CROPCLASS-2.0-unidentified parcel classification output implementing diverse prediction model types.

<p>Partial view of the CROPCLASS-2.0-unidentified parcel classification output implementing diverse prediction model types.</p