Cloud detection plays an essential role in meteorological research and has received considerable attention in recent years. However, this issue is particularly challenging due to the diverse ...

The segmentation of sky images into regions of cloud and clear sky allows atmospheric scientists to determine the fraction of cloud cover and the distribution of cloud without resorting to subjective ...

Cloud and Sun Detection: Binary segmentation of clouds and sun regions from Landsat sky images. Optical Flow Analysis: Lucas-Kanade algorithm to track cloud motion vectors. Centroid Calculation: ...

Deep-learning architecture classify and identify cloud structure on sky infrared images. Standard Convolutional Neural Network (CNN) distinguishes clear sky images from cloud images. An UNet-based ...

Cloud detection in remote sensing images is a fundamental pre-processing step that underpins accurate Earth observation analyses. Over the past decades, researchers have developed a wide range of ...

Until recently, segmentation required large, compute-intensive neural networks. This made it difficult to run these deep learning models without a connection to cloud servers. In their latest work ...

The OHS image has 32 bands with a strong correlation between bands and a large amount of redundant information. Meanwhile, some studies have noted that up to 90% of the spectral bands are unnecessary ...

DeepLab 3+, on the other hand, prioritizes segmentation speed. Trained on the open source PASCAL VOC 2012 image corpus using Google’s TensorFlow machine learning framework on the latest ...

On the other hand, image segmentation involves partitioning an image into multiple segments or regions, where each segment corresponds to a different object or part of an object. The goal is to label ...

Semantic Segmentation: The process of classifying each pixel in an image into a particular category, such as cloud or clear sky, to create a detailed map of the scene.