Comparative Analysis of Methods for Cloud Segmentation in Ground-Based Infrared Images

TL;DR

This paper compares discriminative and generative models for real-time cloud segmentation in ground-based infrared images, highlighting the effectiveness of Markov Random Fields and primal discriminative models.

Contribution

It provides a comprehensive comparison of supervised and unsupervised models, emphasizing real-time feasibility and the impact of preprocessing and feature inclusion.

Findings

Markov Random Fields outperform other models in accuracy.

Primal discriminative models offer high performance with lower computation time.

Preprocessing improves segmentation performance across methods.

Abstract

The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generative models for cloud segmentation. The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated. The discriminative models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. Infrared image preprocessing to remove stationary artifacts increases the overall performance in the analyzed methods. The inclusion of features from neighboring pixels in the feature vectors leads to a performance improvement in some of the cases. Markov Random Fields achieve the best performance in both unsupervised and supervised generative models. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation. Generative and discriminative models are comparable when preprocessing is applied to the infrared images.