Convolutional neural networks for on-board cloud screening

AcloudscreeningunitonasatelliteplatformforEarthobservationcanplayanimportant role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art

Система класифікації та сегментації зображень хмар із використанням мереж глибокого навчання

Магістерська дисертація: 103 с., 31 рис., 27 табл., 1 додаток, 40 джерел. Об’єктом дослідження є зображення хмарного неба. Предметом дослідження є методи обробки зображень та штучні нейронні мережі для класифікації та сегментації зображень. Мета роботи – проаналізувати зображення хмарного неба, зробленого з поверхні Землі, дослідження існуючих підходів та розробка програмного забезпечення. Виконано аналіз методів автоматичної класифікації та сегментації. Було розроблено модель нейронної мережі для класифікації зображень хмар. В результаті було розроблено програмний продукт, який дозволяє класифікувати та сегментувати зображення хмар.Master's thesis: 103 pages, 31 figures, 27 tables, 1 appendix, 40 sources. The object of study is the image of a cloudy sky. The subject of research is image processing methods and artificial neural networks for image classification. The purpose of the work is to analyze the image of the cloudy sky made from the Earth's surface, study existing approaches and develop software. The analysis of methods of automatic classification and segmentation is performed. A neural network model was developed to classify cloud images. As a result, a software product was developed that allows you to classify and segment cloud images

Deep Learning for Image Analysis in Satellite and Traffic Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Utilizing Multilevel Features for Cloud Detection on Satellite Imagery

Cloud detection, which is defined as the pixel-wise binary classification, is significant in satellite imagery processing. In current remote sensing literature, cloud detection methods are linked to the relationships of imagery bands or based on simple image feature analysis. These methods, which only focus on low-level features, are not robust enough on the images with difficult land covers, for clouds share similar image features such as color and texture with the land covers. To solve the problem, in this paper, we propose a novel deep learning method for cloud detection on satellite imagery by utilizing multilevel image features with two major processes. The first process is to obtain the cloud probability map from the designed deep convolutional neural network, which concatenates deep neural network features from low-level to high-level. The second part of the method is to get refined cloud masks through a composite image filter technique, where the specific filter captures multilevel features of cloud structures and the surroundings of the input imagery. In the experiments, the proposed method achieves 85.38% intersection over union of cloud in the testing set which contains 100 Gaofen-1 wide field of view images and obtains satisfactory visual cloud masks, especially for those hard images. The experimental results show that utilizing multilevel features by the combination of the network with feature concatenation and the particular filter tackles the cloud detection problem with improved cloud masks