3. Producción

Browse

Settings

Author: search.filters.author.César BeltránSubject: search.filters.subject.Segmentation

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Application of semantic segmentation with few labels in the detection of water bodies from PeruSat-1 satellite’s images
    (Copernicus Publications, 2020-11-06)
    Abstract. Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semiautomatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts.In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improved.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Cost-effective and portable device for partial shading assessment in photovoltaic modules using RGB imaging
    (Elsevier BV, 2026-02-01)
    Partial shading conditions in photovoltaic systems can cause major loss in performance. Therefore, models to estimate the reduction and distribution of irradiance due to partial shading are important to evaluate and minimize losses. In this paper, a model to estimate the irradiance reduction under partial shading conditions on PV installations is discussed. The proposed approach utilizes RGB images in the visible spectrum to estimate irradiance within the shaded areas of individual modules. The results suggest that there is a significant correlation between gray scale levels and irradiance in the shaded area. The conclusion is based on comprehensive observations and data collected during the experimental phase, which demonstrate regularity in irradiance values within the shaded area. The key advantage of the proposed methodology is its practical framework, which enables it to address various problems and types of partial shading, regardless of the complexity of the geometry. Furthermore, a model was proposed and validated based on this methodology. It is demonstrated that considering partial shading through RGB images, and by employing segmentation, homogenization, and regression using analytic models, this leads to an estimation accuracy in the irradiance parameter (RMSE of ∼1 %) of 11.14 W/m2. This innovative methodology eliminates the need for multiple irradiance sensors or an IV curve tracer to map shadowed zones.
      3