scholarly journals ANALYSIS OF COMBINED UAV-BASED RGB AND THERMAL REMOTE SENSING DATA: A NEW APPROACH TO CROWD MONITORING

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 347-354
Author(s):  
S. Schulte ◽  
F. Hillen ◽  
T. Prinz
Keyword(s):  
Remote Sensing ◽  
Information Needs ◽  
Remote Sensing Data ◽  
Specific Information ◽  
Thermal Remote Sensing ◽  
Sensing Data ◽  
Detection Approach ◽  
Remotely Piloted Aircraft ◽  
Crowd Monitoring ◽  
Single Data

Collecting vast amount of data does not solely help to fulfil information needs related to crowd monitoring, it is rather important to collect data that is suitable to meet specific information requirements. In order to address this issue, a prototype is developed to facilitate the combination of UAV-based RGB and thermal remote sensing datasets. In an experimental approach, image sensors were mounted on a remotely piloted aircraft and captured two video datasets over a crowd. A group of volunteers performed diverse movements that depict real world scenarios. The prototype is deriving the movement on the ground and is programmed in MATLAB. This novel detection approach using combined data is afterwards evaluated against detection algorithms that only use a single data source. Our tests show that the combination of RGB and thermal remote sensing data is beneficial for the field of crowd monitoring regarding the detection of crowd movement.

Thermal remote sensing data enhancement over Alpine Vegetated Areas for evapotranspiration modelling

2020 ◽  
Author(s):  
Paulina Bartkowiak ◽  
Mariapina Castelli ◽  
Roberto Colombo ◽  
Claudia Notarnicola
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Spatial Resolution ◽  
Missing Values ◽  
Remote Sensing Data ◽  
European Alps ◽  
Thermal Remote Sensing ◽  
Sensing Data ◽  
Fine Resolution ◽  
Availability Assessment

<p>The main objective of this study is to exploit thermal remote sensing data for evapotranspiration (ET) modelling in the European Alps. This geographic region has been noted as a hot spot of climate change triggered by increasing number of drought events in recent years, with impacts on natural and agricultural vegetation. Evapotranspiration is considered as one of the major indicators for examining water anomalies in plants. The state-of-art ET models exploiting thermal remote sensing data have shown a large potential in water cycle monitoring. However, existing satellite-derived products do not provide adequate spatial resolution for mountain ecosystems affected by complex orography, common overcast and land-cover heterogeneity. Even though fine resolution imagery fills the gap regarding non-homogenous areas, its long revisit time and frequent cloud contamination hamper spatially continuous ET modelling. In this context, our aim is to overcome these limitations by downscaling and gap-filling 1-km MODIS LST (MOD11A1) to retrieve daily LST maps at 250 m spatial resolution, which can be considered a reasonable scale in the selected area. Firstly, we downscale MODIS LST images with the Random Forest (RF) algorithm by exploiting the relationship between coarse resolution MODIS LST and 250-m explanatory variables, including digital elevation model and normalized difference vegetation index. The 1-km MODIS LST and the downscaled product were compared with fine resolution Landsat LST images. The random forest results show an improvement of about 20% in the agreement between Landsat and 250-m MODIS LST compared to statistics obtained for MOD11A1. Secondly, we propose to recover missing values of LST pixels beneath the clouds. Considering local-scale climate variability of the study area, we present a novel approach based on investigating the relationships between LST and meteorological data under clear- and cloudy-sky conditions. The abovementioned improvements are planned to be used for energy balance modelling of ET with relevant implications on water availability assessment in the Alpine region.</p>

Combining optical and thermal remote sensing data for mapping debris-covered glaciers (Alamkouh Glaciers, Iran)

2012 ◽  
Vol 71 ◽  
pp. 73-83 ◽  
Author(s):  
Neamat Karimi ◽  
Ashkan Farokhnia ◽  
Laila Karimi ◽  
Morteza Eftekhari ◽  
Hossain Ghalkhani
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Thermal Remote Sensing ◽  
Sensing Data

scholarly journals A New Unsupervised Change Detection Approach Based On DWT Image Fusion And Backtracking Search Optimization Algorithm For Optical Remote Sensing Data

2014 ◽  
Vol XL-7 ◽  
pp. 15-18 ◽  
Author(s):  
U. H. Atasever ◽  
P. Civicioglu ◽  
E. Besdok ◽  
C. Ozkan
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Backtracking Search ◽  
Search Optimization ◽  
Detection Approach ◽  
Multi Temporal ◽  
Backtracking Search Optimization Algorithm

Change detection is one of the most important subjects of remote sensing discipline. In this paper, a new unsupervised change detection approach is proposed for multi-temporal remotely sensed optic imagery. This approach does not require any prior information about changed and unchanged pixels. The approach is based on Discrete Wavelet Transform (DWT) based image fusion and Backtracking Search Optimization Algorithm (BSA). In the first step of the approach, absolute-valued difference image and absolute-valued log-ratio image is calculated from co-registered and radiometrically corrected multi-temporal images. Then, these difference images are fused using DWT. The fused image is filtered by median filter for edge information preservation and by wiener filter for image smoothing. Then, a min-max normalization is applied to the filtered data. The normalized data is clustered into two groups with BSA as changed and unchanged pixels by minimizing an objective function, unlike classical methods using CVA, PCA, FCM or K-means techniques. To show effectiveness of proposed approach, two remote sensing data sets, Sardinia and Mexico, are used. False Alarm, Missed Alarm, Total Alarm and Total Error Rate are selected as performance criteria to evaluate the effectiveness of new approach using ground truth images. Experimental results show that proposed approach is effective for unsupervised change detection of optical remote sensing data.

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