Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

2015 ◽  
Vol 17 (3) ◽  
pp. 332-348 ◽  
Author(s):  
Lin Yuan ◽  
Ruiliang Pu ◽  
Jingcheng Zhang ◽  
Jihua Wang ◽  
Hao Yang
Keyword(s):  
Powdery Mildew ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Regional Scale

scholarly journals Satellite imagery of lengthy territories with complex configuration with the account of attitude and positioning errors

2019 ◽  
Vol 75 ◽  
pp. 01013
Author(s):  
Dmitriy Mozgovoy ◽  
Dmitriy Svinarenko ◽  
Roman Tsarev ◽  
Tatiana Yamskikh
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Program Control ◽  
Complex Configuration ◽  
Positioning Errors ◽  
Satellite Attitude ◽  
The Impact

A method for monitoring attitude and positioning errors when taking satellite imagery of lengthy territories with complex configuration using an ultra-high spatial resolution optical-electronic scanner is described in the article. The results of modeling the system of automatic satellite attitude program control during the process of imagery are presented. Given these results, the impact of attitude and positioning errors during satellite imagery was estimated on the coverage percentage of the territory to be imaged.

Coastal wetland classification with multiseasonal high-spatial resolution satellite imagery

2018 ◽  
Vol 39 (23) ◽  
pp. 8963-8983 ◽  
Author(s):  
Nan Li ◽  
Dengsheng Lu ◽  
Ming Wu ◽  
Yinlong Zhang ◽  
Linying Lu
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Coastal Wetland ◽  
High Spatial Resolution ◽  
Wetland Classification

scholarly journals Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 185 ◽  
Author(s):  
Christopher A. Ramezan ◽  
Timothy A. Warner ◽  
Aaron E. Maxwell
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Spatial Resolution ◽  
Cross Validation ◽  
High Spatial Resolution ◽  
Regional Scale ◽  
Sample Selection ◽  
Selection Methods ◽  
Machine Learning Classification ◽  
Leave One Out

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided.

scholarly journals Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

2017 ◽  
Vol 191 ◽  
pp. 95-109 ◽  
Author(s):  
Ran Meng ◽  
Jin Wu ◽  
Kathy L. Schwager ◽  
Feng Zhao ◽  
Philip E. Dennison ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Spatial Scales ◽  
Burn Severity ◽  
Pine Barrens

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

2015 ◽  
Author(s):  
Saad A. Alsharrah ◽  
David A. Bruce ◽  
Rachid Bouabid ◽  
Sekhar Somenahalli ◽  
Paul A. Corcoran
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Desert Plants

HIGH SPATIAL RESOLUTION MAPPING OF MALARIA TRANSMISSION RISK IN THE GAMBIA, WEST AFRICA, USING LANDSAT TM SATELLITE IMAGERY

2007 ◽  
Vol 76 (5) ◽  
pp. 875-881 ◽  
Author(s):  
CLAUS BØGH ◽  
MARGARET PINDER ◽  
MUSA JAWARA ◽  
ANDY DEAN ◽  
CHRISTOPHER J. THOMAS ◽  
...  
Keyword(s):  
West Africa ◽  
Malaria Transmission ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
The Gambia ◽  
Landsat Tm ◽  
Transmission Risk ◽  
Resolution Mapping
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