scholarly journals Extraction of Road Lanes from High-Resolution Stereo Aerial Imagery Based on Maximum Likelihood Segmentation and Texture Enhancement

2009 ◽  
Author(s):  
Hang Jin ◽  
Yanming Feng ◽  
Zhengrong Li
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Aerial Imagery

Assessment of residual slash coverage using UAVs and implications for aspen regeneration

2020 ◽  
Vol 8 (1) ◽  
pp. 19-29
Author(s):  
Landon L. Sealey ◽  
Ken C.J. Van Rees
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Image Classification ◽  
Populus Tremuloides ◽  
Classification Accuracy ◽  
Forest Floor ◽  
Aerial Imagery ◽  
Detailed Method ◽  
Slash Distribution ◽  
Supervised Image Classification

Proper redistribution of residual slash following harvesting is crucial for ensuring successful regeneration and continued health in trembling aspen (Populus tremuloides) forests. As traditional methods of measuring residual slash are a strenuous and tedious process, the objective of this study was to develop a new, faster, and more detailed method to assess residual slash distribution for entire harvested blocks. This study also aimed to assess the influence residual slash coverage had on the success of aspen regeneration 1 year after winter harvesting. Using high-resolution UAV imagery and maximum likelihood supervised image classification, residual slash was differentiated from the underlying forest floor. Overall, classification accuracy ranged between 85% and 96% with the highest accuracy occurring when aerial imagery was collected at the beginning of the second spring following winter harvesting. Slash distribution was quite consistent across harvested blocks, with 92% of harvested blocks experiencing <33% coverage. There was no relationship between the level of aspen regeneration following 1 year of growth and percentage slash coverage up to 60%. No vegetation plots occurred in areas with >60% slash coverage; therefore, it is unknown whether aspen regeneration will be affected in areas with higher slash coverage.

scholarly journals Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 193
Author(s):  
Ali Alghamdi ◽  
Anthony R. Cummings
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Accuracy Assessment ◽  
Middle Eastern ◽  
Object Oriented ◽  
Training Data ◽  
High Resolution Imagery ◽  
Significant Research ◽  
Desert Areas ◽  
Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

Estimating SpO2 via Time-Efficient High-Resolution Harmonics Analysis and Maximum Likelihood Tracking

2018 ◽  
Vol 22 (4) ◽  
pp. 1075-1086 ◽  
Author(s):  
Feiyi Fan ◽  
Yuepeng Yan ◽  
Kun Zhao ◽  
Fei Long ◽  
Hao Zhang
Keyword(s):  
Maximum Likelihood ◽  
High Resolution

Maximum likelihood positioning algorithm for high-resolution PET scanners

2016 ◽  
Vol 43 (6Part1) ◽  
pp. 3049-3061 ◽  
Author(s):  
Nicolas Gross-Weege ◽  
David Schug ◽  
Patrick Hallen ◽  
Volkmar Schulz
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Positioning Algorithm ◽  
Pet Scanners

scholarly journals Detection and spatiotemporal analysis of methane ebullition on thermokarst lake ice using high-resolution optical aerial imagery

2016 ◽  
Vol 13 (1) ◽  
pp. 27-44 ◽  
Author(s):  
P. R. Lindgren ◽  
G. Grosse ◽  
K. M. Walter Anthony ◽  
F. J. Meyer
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Image Acquisition ◽  
Optical Image ◽  
Aerial Imagery ◽  
Accurate Estimation ◽  
Lake Ice ◽  
Thermokarst Lakes ◽  
Thermokarst Lake ◽  
Methane Ebullition

Abstract. Thermokarst lakes are important emitters of methane, a potent greenhouse gas. However, accurate estimation of methane flux from thermokarst lakes is difficult due to their remoteness and observational challenges associated with the heterogeneous nature of ebullition. We used high-resolution (9–11 cm) snow-free aerial images of an interior Alaskan thermokarst lake acquired 2 and 4 days following freeze-up in 2011 and 2012, respectively, to detect and characterize methane ebullition seeps and to estimate whole-lake ebullition. Bubbles impeded by the lake ice sheet form distinct white patches as a function of bubbling when lake ice grows downward and around them, trapping the gas in the ice. Our aerial imagery thus captured a snapshot of bubbles trapped in lake ice during the ebullition events that occurred before the image acquisition. Image analysis showed that low-flux A- and B-type seeps are associated with low brightness patches and are statistically distinct from high-flux C-type and hotspot seeps associated with high brightness patches. Mean whole-lake ebullition based on optical image analysis in combination with bubble-trap flux measurements was estimated to be 174 ± 28 and 216 ± 33 mL gas m−2 d−1 for the years 2011 and 2012, respectively. A large number of seeps demonstrated spatiotemporal stability over our 2-year study period. A strong inverse exponential relationship (R2 >  =  0.79) was found between the percent of the surface area of lake ice covered with bubble patches and distance from the active thermokarst lake margin. Even though the narrow timing of optical image acquisition is a critical factor, with respect to both atmospheric pressure changes and snow/no-snow conditions during early lake freeze-up, our study shows that optical remote sensing is a powerful tool to map ebullition seeps on lake ice, to identify their relative strength of ebullition, and to assess their spatiotemporal variability.

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