Cloud detection in satellite images for tropical regions

1996 ◽  
Author(s):  
Anthony T. S. Ho ◽  
Zhenlei Cai
Keyword(s):  
Satellite Images ◽  
Cloud Detection ◽  
Tropical Regions

scholarly journals Mount Semeru Eruption on December 1, 2020: A Volcanic Ash Cloud Assessment Using TIR Imagery with 8.5-12.0 µm Spectral Bands

2020 ◽  
Author(s):  
Andri Wibowo
Keyword(s):  
Brightness Temperature ◽  
Volcanic Ash ◽  
Satellite Images ◽  
Cloud Detection ◽  
Lower Altitude ◽  
Spectral Bands ◽  
Volcanic Ash Cloud ◽  
Ir Bands ◽  
Ash Cloud ◽  
Active Volcanoes

Mount Semeru is one of the most active volcanoes in the Java Island. This article presents the results of observations and detections of volcanic ash cloud after Mt Semeru eruptions on 1 December 2020 at 01:23 AM. Volcanic ash cloud detection was conducted by analyzing thermal infrared (TIR) satellite images acquired by the NOAA-20 and SNPP with MODIS and VIIRS instruments. The TIR instruments have detected the presence of volcanic ash cloud. The results show increasing ash cloud brightness temperature (BT) from 240 to 270 Kelvin (K) several hours after eruptions. Increasing BT indicated the development of volcanic Cumulonimbus (Cb) at lower altitude. Northeast movements of 270 K BT clouds were observed at 06:12 AM. Presences of volcanic Cb and SO2 were confirmed using IR bands of 12.0-10.8 µm, 11.0-8.5µm and 11.0 µm. This Cb cloud was observed moving northeast directions. The data acquired from the TIR imagery resulted from this study is thought be used in future to support and complement ground-based observations and detections of active volcanoes mainly in Java Island.

scholarly journals Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network

2021 ◽  
Vol 13 (16) ◽  
pp. 3319
Author(s):  
Nan Ma ◽  
Lin Sun ◽  
Chenghu Zhou ◽  
Yawen He
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Spectral Library ◽  
Remote Sensing Images ◽  
Different Types ◽  
Sentinel 2

Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow.

scholarly journals AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

2016 ◽  
Vol XLI-B3 ◽  
pp. 475-479 ◽  
Author(s):  
Nicolas Champion
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Growing ◽  
Shadow Detection ◽  
The Other ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Large Area ◽  
The Difference

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled <i>seeds</i> if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled <i>shadows</i> if the difference of reflectance (in the NIR channel) with the <i>synthetic</i> ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled <i>clouds</i> during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

scholarly journals On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

2013 ◽  
Vol 6 (1) ◽  
pp. 1093-1141
Author(s):  
K.-G. Karlsson ◽  
E. Johansson
Keyword(s):  
Satellite Imagery ◽  
Winter Season ◽  
Polar Regions ◽  
Cloud Detection ◽  
Optimal Method ◽  
Tropical Regions ◽  
Boundary Layer Clouds ◽  
Temperature Contrast ◽  
Land Surfaces ◽  
High Level

Abstract. A method for detailed evaluation of a new satellite-derived global 28-yr cloud and radiation climatology (Climate Monitoring SAF Cloud, Albedo and Radiation dataset from AVHRR data, named CLARA-A1) from polar orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations. Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications generating erroneous clouds over land surfaces in semi-arid regions in the sub-tropical and tropical regions are revealed. In addition, a substantial fraction of all clouds remains undetected in the Polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds. Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of −274 m was achieved compared to a bias of −2762 m if no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations while the opposite is true for low-level (boundary layer) clouds. The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery.

Cloud detection in satellite images using multi-objective social spider optimization

2019 ◽  
Vol 79 ◽  
pp. 203-226 ◽  
Author(s):  
Rachana Gupta ◽  
Satyasai Jagannath Nanda ◽  
Urvashi Prakash Shukla
Keyword(s):  
Satellite Images ◽  
Cloud Detection ◽  
Social Spider ◽  
Multi Objective ◽  
Social Spider Optimization

scholarly journals Goes-13 IR Images for Rainfall Forecasting in Hurricane Storms

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 85-101
Author(s):  
Marilu Meza-Ruiz ◽  
Alfonso Gutierrez-Lopez
Keyword(s):  
Early Warning ◽  
Satellite Images ◽  
Early Warning Systems ◽  
The United States ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Systematic Analysis ◽  
Tropical Regions ◽  
Surface Precipitation ◽  
Analysis Of The Images

Currently, it is possible to access a large amount of satellite weather information from monitoring and forecasting severe storms. However, there are no methods of employing satellite images that can improve real-time early warning systems in different regions of Mexico. The auto-estimator is the most commonly used technique that was developed for specific locations in the United States of America (32°–49° latitude) for the type of convective storms. However, the estimation of precipitation intensities for meteorological conditions in tropic latitudes, using the auto-estimator technique, needs to be re-adjusted and calibrated. It is necessary to improve this type of technique that allows decision-makers to have hydro-informatic tools capable of improving early warning systems in tropical regions (15°–25° Mexican tropic latitude). The main objective of the work is to estimate rainfall from satellite imagery in the infrared (IR) spectrum from the Geostationary Operational Environmental Satellite (GOES), validating these estimates with a network of surface rain gauges. Using the GOES-13 IR images every 15 min and using the auto-estimator, a downscaling of six hurricanes was performed from which surface precipitation events were measured. The two main difficulties were to match the satellite images taken every 15 min with the surface data measured every 10 min and to develop a program in C+ that would allow the systematic analysis of the images. The results of this work allow us to get a new adjustment of coefficients in a new equation of the auto-estimator, valid for rain produced by hurricanes, something that has not been done until now. Although no universal relationship has been found for hurricane rainfall, it is evident that the original formula of the auto-estimator technique needs to be modified according to geographical latitude.

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