Soumyabrata Dev

WAHRSIS: A Low-cost, High-resolution Whole Sky Imager With Near-Infrared Capabilities

Cloud imaging using ground-based whole sky imagers is essential for a fine-grained understanding of cloud formations, which can be useful in many applications. Some such imagers are available commercially, but their cost is relatively high, and their flexibility is limited. Therefore, we built a new daytime Whole Sky Imager (WSI) called Wide Angle High-Resolution Sky Imaging System (WAHRSIS). The strengths of our new design are its simplicity, low manufacturing cost, and high image resolution. Our imager captures the entire hemisphere in a single picture using a digital camera with a Fish-eye lens. The camera was modified to capture light across the visible and near-infrared spectral ranges. This paper describes the design of the device as well as the geometric and radiometric calibration of the imaging system.

Systematic study of color spaces and components for the segmentation of sky/cloud images

Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns. The accurate segmentation of clouds in these images is a challenging task, as clouds do not possess any clear structure. Several algorithms using different color models have been proposed in the literature. This paper presents a systematic approach for the selection of color spaces and components for optimal segmentation of sky/cloud images. Using mainly principal component analysis (PCA) and fuzzy clustering for evaluation, we identify the most suitable color components for this task.

Design of low-cost, compact and weather-proof whole sky imagers for high-dynamic-range captures

Ground-based whole sky imagers are popular for monitoring cloud formations, which is necessary for various applications. We present two new Wide Angle High-Resolution Sky Imaging System (WAHRSIS) models, which were designed especially to withstand the hot and humid climate of Singapore. The first uses a fully sealed casing, whose interior temperature is regulated using a Peltier cooler. The second features a double roof design with ventilation grids on the sides, allowing the outside air to flow through the device. Measurements of temperature inside these two devices show their ability to operate in Singapore weather conditions. Unlike our original WAHRSIS model, neither uses a mechanical sun blocker to prevent the direct sunlight from reaching the camera; instead they rely on high-dynamic-range imaging (HDRI) techniques to reduce the glare from the sun.

Categorization of cloud image patches using an improved texton-based approach

We propose a modified texton-based classification approach that integrates both color and texture information for improved classification results. We test our proposed method for the task of cloud classification on SWIMCAT, a large new database of cloud images taken with a ground-based sky imager, with very good results. We perform an extensive evaluation, comparing different color components, filter banks, and other parameters to understand their effect on classification accuracy. Finally, we release the SWIMCAT dataset that was created for the task of cloud categorization.

Color-Based Segmentation of Sky/Cloud Images From Ground-Based Cameras

Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers) are increasingly used nowadays because of their applications in a number of fields, including climate modeling, weather prediction, renewable energy generation, and satellite communications. Due to the wide variety of cloud types and lighting conditions in such images, accurate and robust segmentation of clouds is challenging. In this paper, we present a supervised segmentation framework for ground-based sky/cloud images based on a systematic analysis of different color spaces and components, using partial least-squares regression. Unlike other state-of-the-art methods, our proposed approach is entirely learning based and does not require any manually defined parameters. In addition, we release the Singapore whole Sky imaging segmentation database, a large database of annotated sky/cloud images, to the research community.

Ground-Based Image Analysis: A Tutorial on Machine-Learning Techniques and Applications

Ground-based whole-sky cameras have opened up new opportunities for monitoring the earths atmosphere. These cameras are an important complement to satellite images by providing geoscientists with cheaper, faster, and more localized data. The images captured by whole-sky imagers (WSI) can have high spatial and temporal resolution, which is an important prerequisite for applications such as solar energy modeling, cloud attenuation analysis, local weather prediction, and more. Extracting the valuable information from the huge amount of image data by detecting and analyzing the various entities in these images is challenging. However, powerful machine-learning techniques have become available to aid with the image analysis. This article provides a detailed explanation of recent developments in these techniques and their applications in ground-based imaging, aiming to bridge the gap between computer vision and remote sensing with the help of illustrative examples. We demonstrate the advantages of using machine-learning techniques in ground-based image analysis via three primary applications: segmentation, classification, and denoising.

Cloud base height estimation using high-resolution whole sky imagers

Fine scale cloud monitoring using ground-based imagers is becoming popular for a variety of applications and domains. We present a framework for cloud base height estimation using two such imagers; our method is based on stereoscopic scene flow. We demonstrate the feasibility of our approach and use computer-generated images with controlled cloud height to validate the accuracy of our method.

Multi-level semantic labeling of Sky/cloud images

Sky/cloud images captured by ground-based Whole Sky Imagers (WSIs) are extensively used now-a-days for various applications. In this paper, we learn the semantics of sky/cloud images, which allows an automatic annotation of pixels with different class labels. We model the various labels/classes with a continuous-valued multi-variate distribution. Using a set of training images, the distributions for different labels are learnt, and subsequently used for labeling test images. We also present a method to determine the number of clusters. Our proposed approach is the first for multi-class sky-cloud image annotation and achieves very good results.

GPS derived PWV for rainfall monitoring

Precipitable Water Vapor (PWV) is a good source to monitor precipitation. It is defined by the amount of water vapor present in atmosphere. Traditionally, radiosondes and microwave radiometers were used to derive PWV. However, these devices have poor temporal resolutions and high operational costs. Therefore, GPS signal delay is now widely used for such purposes. The main aim of this paper is to study relationship between GPS derived PWV and precipitation. We present an analysis which shows that PWV increases before any rainfall event, while it decreases after the rainfall event. We also derive a threshold PWV that detects the occurrence of rainfall, once PWV exceeds the threshold value. PWV and rainfall data of June 2010 and 2011 are used for validation.

Estimation of solar irradiance using ground-based whole sky imagers

Ground-based whole sky imagers (WSIs) can provide localized images of the sky of high temporal and spatial resolution, which permits fine-grained cloud observation. In this paper, we show how images taken by WSIs can be used to estimate solar radiation. Sky cameras are useful here because they provide additional information about cloud movement and coverage, which are otherwise not available from weather station data. Our setup includes ground-based weather stations at the same location as the imagers. We use their measurements to validate our methods.