A new approach to removing cloud cover from satellite imagery

1984 ◽  
Vol 25 (2) ◽  
pp. 252-256 ◽  
Author(s):  
Z.K Liu ◽  
B.R Hunt
Keyword(s):  
Satellite Imagery ◽  
Cloud Cover ◽  
New Approach

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

Optical Satellite Multiobjective Genetic Algorithms for Investigation of MH370 Debris

2020 ◽  
pp. 166-207
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cloud Cover ◽  
Optical Remote Sensing ◽  
New Approach ◽  
Precise Method ◽  
Spectral Signatures ◽  
Remote Sensing Technique ◽  
Multiobjective Genetic Algorithms ◽  
Strength Constraints

At present, there is no precise method that can inform where the lost flight MH370 is. This chapter proposes a new approach to search for the missing flight MH370. To this end, multiobjective genetic algorithms are implemented. In this regard, a genetic algorithm is taken into consideration to optimize the MH370 debris that is notably based on the geometrical shapes and spectral signatures. Currently, there may be three limitations to optical remote sensing technique: (1) strength constraints of the spacecraft permit about two hours of scanning consistently within the day, (2) cloud cover prevents unique observations, and (3) moderate information from close to the ocean surface is sensed through the scanner. Needless to say that the objects that are spotted by different satellite data do not scientifically belong to the MH370 debris and could be just man-made without accurate identifications.

scholarly journals The scale of the whale: using video-tag data to evaluate sea-surface ice concentration from the perspective of individual Antarctic minke whales

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Jacob M. J. Linsky ◽  
Nicole Wilson ◽  
David E. Cade ◽  
Jeremy A. Goldbogen ◽  
David W. Johnston ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Antarctic Peninsula ◽  
Environmental Variables ◽  
Cloud Cover ◽  
Video Data ◽  
Environmental Data ◽  
Minke Whales ◽  
Ice Concentration ◽  
Surface Ice ◽  
The Antarctic

Abstract Background Advances in biologging technology allow researchers access to previously unobservable behavioral states and movement patterns of marine animals. To relate behaviors with environmental variables, features must be evaluated at scales relevant to the animal or behavior. Remotely sensed environmental data, collected via satellites, often suffers from the effects of cloud cover and lacks the spatial or temporal resolution to adequately link with individual animal behaviors or behavioral bouts. This study establishes a new method for remotely and continuously quantifying surface ice concentration (SIC) at a scale relevant to individual whales using on-animal tag video data. Results Motion-sensing and video-recording suction cup tags were deployed on 7 Antarctic minke whales (Balaenoptera bonaerensis) around the Antarctic Peninsula in February and March of 2018. To compare the scale of camera-tag observations with satellite imagery, the area of view was simulated using camera-tag parameters. For expected conditions, we found the visible area maximum to be ~ 100m2 which indicates that observations occur at an equivalent or finer scale than a single pixel of high-resolution visible spectrum satellite imagery. SIC was classified into one of six bins (0%, 1–20%, 21–40%, 41–60%, 61–80%, 81–100%) by two independent observers for the initial and final surfacing between dives. In the event of a disagreement, a third independent observer was introduced, and the median of the three observer’s values was used. Initial results (n = 6) show that Antarctic minke whales in the coastal bays of the Antarctic Peninsula spend 52% of their time in open water, and only 15% of their time in water with SIC greater than 20%. Over time, we find significant variation in observed SIC, indicating that Antarctic minke occupy an extremely dynamic environment. Sentinel-2 satellite-based approaches of sea ice assessment were not possible because of persistent cloud cover during the study period. Conclusion Tag-video offers a means to evaluate ice concentration at spatial and temporal scales relevant to the individual. Combined with information on underwater behavior, our ability to quantify SIC continuously at the scale of the animal will improve upon current remote sensing methods to understand the link between animal behavior and these dynamic environmental variables.

scholarly journals Adaptive Contrast Enhancement of Optical Imagery Based on Level of Detail (LOD)

2020 ◽  
Vol 12 (10) ◽  
pp. 1555
Author(s):  
Cheng-Chien Liu
Keyword(s):  
Satellite Imagery ◽  
World Wide ◽  
Level Of Detail ◽  
Decision Makers ◽  
Visual Effects ◽  
New Approach ◽  
Web Mapping ◽  
The World ◽  
Optical Imagery ◽  
The One

The viewing and sharing of remote sensing optical imagery through the World Wide Web is an efficient means for providing information to the general public and decision makers. Since clouds and hazes inevitably limit the contrast and deteriorate visual effects, only cloudless scenes are usually included and presented in existing web mapping services. This work proposes a level-of-detail (LOD) based enhancement approach to present satellite imagery with an adaptively enhanced contrast determined by its viewing LOD. Compared to existing web mapping services, this new approach provides a better visual effect as well as spectral details of satellite imagery for cases partially covered with clouds or cirrocumulus clouds. The full archive of global satellite imagery, either the existing one or the one collected in the future, can be utilized and shared through the Web with the processing proposed in this new approach.

scholarly journals PERBANDINGAN ANTARA INFORMASI SUHU PERMUKAAN LAUT DARI DATA SATELIT DENGAN HASIL PEMODELAN DI WPP NRI-716

1970 ◽  
Vol 16 (1) ◽  
Author(s):  
Komang Iwan Suniada
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Sea Surface ◽  
Surface Information ◽  
Sst Model ◽  
Cloud Modeling ◽  
Modeling Data ◽  
Satellite Sensor

Sea Surface Temperature (SST) data and information recently become a valuableinformation since its association with the climate, oceanography condition and fisherieshave been discovered.  Unfortunately, SST information using satellite imagery frequentlyconstrained by atmospheric cloud cover since satellite sensor disability to gather any landor ocean surface information through the cloud.  Modeling data is very required to fill theblank data resulted from satellite imagery under cloudy condition.  This study conducted atSulawesi Sea to North Halmahera which is included to Fisheries Managing Area (FMA)716, to find out the strength and direction relationship between SST model and SST satellite.Result indicates there is a strong and same direction relationship between SST model andSST satellite (r=0.704, n=1516) with 0.2C diferrence so that SST model can be used to fillor substitute the blank of SST satellite.

Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service

2005 ◽  
Vol 35 (12) ◽  
pp. 2968-2980 ◽  
Author(s):  
Ronald E McRoberts ◽  
Geoffrey R Holden ◽  
Mark D Nelson ◽  
Greg C Liknes ◽  
Dale D Gormanson
Keyword(s):  
Satellite Imagery ◽  
Forest Inventory ◽  
Forest Service ◽  
Analysis Program ◽  
Ancillary Data ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
New Approach ◽  
Areas Of Interest ◽  
Effective Source

Forest inventory programs report estimates of forest variables for areas of interest ranging in size from municipalities, to counties, to states or provinces. Because of numerous factors, sample sizes are often insufficient to estimate attributes as precisely as is desired, unless the estimation process is enhanced using ancillary data. Classified satellite imagery has been shown to be an effective source of ancillary data that, when used with stratified estimation techniques, contributes to increased precision with little corresponding increase in cost. Stratification investigations conducted by the Forest Inventory and Analysis program of the USDA Forest Service are reviewed, and a new approach to stratification using satellite imagery is proposed. The results indicate that precision may be substantially increased for estimates of both forest area and volume per unit area.

Sign in / Sign up

Export Citation Format

Share Document