Automated Cloud Patch Segmentation of FY-2C Image Using Artificial Neural Network and Seeded Region Growing Method (ANN-SRG)

This paper presents a new algorithm Artificial Neural Network and Seeded Region Growing (ANN-SRG) to segment cloud patches of different types. This method used Seeded Region Growing (SRG) as segmentation algorithm, and Artificial Neural Network (ANN) Cloud classification as preprocessing algorithm. It can be trained to respond favorably to cloud types of interest, and SRG method is no longer sensitive to the seeds selection and growing rule. To illustrate the performance of this technique, this paper applied it on Chinese first operational geostationary meteorological satellite FengYun-2C (FY-2C) in three infrared channels (IR1, 10.3- 11.33BC;m; IR2, 11.5-12.53BC;m and WV 6.3-7.63BC;m) with 2864 samples collected by meteorologists in June, July, and August in 2007. The result shows that this method can distinguish and segment cloud patches of different types, and improves the traditional SRG algorithm by reducing the uncertainty of seeds extraction and regional growth

ОПРЕДЕЛЕНИЕ РАЗМЕРА LIFO-СТЕКА ДЛЯ ВЫРАЩИВАНИЯ ОБЛАСТЕЙ ИЗОБРАЖЕНИЙ

This paper considers the problem of memory allocation for the organization of the LIFO-stack in the algorithm for image segmentation based on growing regions is considered. Segmentation divides the image into regions with identical or similar properties and is the most demanding process for the capacity of RAM. The cultivation of areas begins with the neighborhoods of pre-selected initial growth pixels and uses stacks to store the coordinates of adjacent pixels attached to the cultivated region. Stack loading is maximized when the segment size matches the size of the YX image. In the absence of an expression for the exact determination of the size of the stack, it is possible to guarantee the stable operation of the algorithm for growing regions, eliminating the overflow of the memory allocated for processing if the stack size is assumed equal to YX. However, this approach does not take into account the fact that filling the coordinate stacks is also accompanied by a selection of them, which makesthe stack size always smaller than YX. The article proposes an expression that allows one to increase the accuracy of determining the required size of the LIFO-stack for storing the coordinates of adjacent pixels depending on the image size. The expression takes into account the conditions of the maximum load of the LIFO-stack when: a) the segmentation of the square region with the initial growth pixel in the corner of this region is carried out; b) in the scan window, adjacent pixels are always selected in order with the first selectable pixel located in the corner of the scan window. Using the proposed expression to calculate the required capacity of the LIFO-stack under conditions of its maximum load in the image segmentation algorithm based on growing regions provides a 2-fold reduction in the number of LIFO-stack memory cells.Рассматривается задача выделения памяти для организации LIFO-стека в алгоритме сегментации изображений на основе выращивания областей. Сегментация разделяет изображение на области с одинаковыми или схожими свойствами и является наиболее требовательным к емкости оперативной памяти процессом. Выращивание областей начинается с окрестностей предварительно выделенных начальных пикселей роста и использует стеки для хранения координат смежных пикселей, присоединяемых к выращиваемой области. Загрузка стеков максимальна, когда размер сегмента совпадает с размером YX изображения. При отсутствии выражения для точного определения размера стека гарантировать устойчивую работу алгоритма выращивания областей, исключающую переполнение выделенной для обработки памяти, можно, только если размер стека принять равным YX. Однако этот подход не учитывает то, что заполнение стеков координат сопровождается также выборкой из них, вследствие чего размер стека всегда меньше YX. В статье предлагается выражение, позволяющее повысить точность определения необходимого размера LIFO-стека для хранения координат смежных пикселей в зависимости от размера изображения. Выражение учитывает условия максимальной загрузки LIFO-стека, когда: а) осуществляется сегментация квадратной области с начальным пикселем роста в углу этой области; б) в окне сканирования смежные пиксели всегда выбираются по порядку с расположением первого выбираемого пикселя в углу окна сканирования. Использование предложенного выражения для расчета необходимой емкости LIFO-стека в условиях его максимальной загрузки в алгоритме сегментации изображений на основе выращивания областей обеспечивает уменьшение числа ячеек памяти LIFO-стека в 2 раза

Applications of Physically Accurate Deep Learning for Processing Digital Rock Images

Digital rock analysis aims to improve our understanding of the fluid flow properties of reservoir rocks, which are important for enhanced oil recovery, hydrogen storage, carbonate dioxide storage, and groundwater management. X-ray microcomputed tomography (micro-CT) is the primary approach to capturing the structure of porous rock samples for digital rock analysis. Initially, the obtained micro-CT images are processed using image-based techniques, such as registration, denoising, and segmentation depending on various requirements. Numerical simulations are then conducted on the digital models for petrophysical prediction. The accuracy of the numerical simulation highly depends on the quality of the micro-CT images. Therefore, image processing is a critical step for digital rock analysis. Recent advances in deep learning have surpassed conventional methods for image processing. Herein, the utility of convolutional neural networks (CNN) and generative adversarial networks (GAN) are assessed in regard to various applications in digital rock image processing, such as segmentation, super-resolution, and denoising. To obtain training data, different sandstone and carbonate samples were scanned using various micro-CT facilities. After that, validation images previously unseen by the trained neural networks are utilised to evaluate the performance and robustness of the proposed deep learning techniques. Various threshold scenarios are applied to segment the reconstructed digital rock images for sensitivity analyses. Then, quantitative petrophysical analyses, such as porosity, absolute/relative permeability, and pore size distribution, are implemented to estimate the physical accuracy of the digital rock data with the corresponding ground truth data. The results show that both CNN and GAN deep learning methods can provide physically accurate digital rock images with less user bias than traditional approaches. These results unlock new pathways for various applications related to the reservoir characterisation of porous reservoir rocks