scholarly journals Four-band Thermal Mosaicking: A New Method to Process Infrared Thermal Imagery of Urban Landscapes from UAV Flights

2019 ◽  
Vol 11 (11) ◽  
pp. 1365 ◽  
Author(s):  
Yichen Yang ◽  
Xuhui Lee
Keyword(s):  
High Resolution ◽  
Image Data ◽  
Calibration Method ◽  
Urban Landscapes ◽  
Thermal Camera ◽  
Thermal Images ◽  
Natural Park ◽  
Object Based ◽  
Novel Method ◽  
Thermal Band

Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form a single image mosaic and to construct a 3-D digital model of the measurement target. However, the mosaicking of thermal images is challenging due to low lens resolution and weak contrast in the single thermal band. In this study, a novel method, referred to as four-band thermal mosaicking (FTM), was developed in order to process thermal images. The method stacks the thermal band obtained by a thermal camera onto the RGB bands acquired on the same flight by an RGB camera and mosaics the four bands simultaneously. An object-based calibration method is then used to eliminate inter-band positional errors. A UAV flight over a natural park was carried out in order to test the method. The results demonstrated that with the assistance of the high-resolution RGB bands, the method enabled successful and efficient thermal mosaicking. Transect analysis revealed an inter-band accuracy of 0.39 m or 0.68 times the ground pixel size of the thermal camera. A cluster analysis validated that the thermal mosaic captured the expected contrast of thermal properties between different surfaces within the scene.

scholarly journals SAR Image Segmentation Using Morphological Attribute Profiles

2014 ◽  
Vol XL-3 ◽  
pp. 39-44 ◽  
Author(s):  
M. Boldt ◽  
A. Thiele ◽  
K. Schulz ◽  
S. Hinz
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Region Growing ◽  
Object Based Image Analysis ◽  
Sar Image Segmentation ◽  
High Resolution Imagery ◽  
Object Based ◽  
High Resolution Images ◽  
Novel Method ◽  
Image Set

In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. <br><br> In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

scholarly journals Deep Learning Based Super Resolution and Classification Applications for Neonatal Thermal Images

2021 ◽  
Vol 38 (5) ◽  
pp. 1361-1368
Author(s):  
Fatih M. Senalp ◽  
Murat Ceylan
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Thermal Imaging ◽  
Similarity Index ◽  
Super Resolution ◽  
Ground Truth ◽  
Structural Similarity ◽  
Generative Adversarial Networks ◽  
Thermal Camera ◽  
Thermal Images

The thermal camera systems can be used in all kinds of applications that require the detection of heat change, but thermal imaging systems are highly costly systems. In recent years, developments in the field of deep learning have increased the success by obtaining quality results compared to traditional methods. In this paper, thermal images of neonates (healthy - unhealthy) obtained from a high-resolution thermal camera were used and these images were evaluated as high resolution (ground truth) images. Later, these thermal images were downscaled at 1/2, 1/4, 1/8 ratios, and three different datasets consisting of low-resolution images in different sizes were obtained. In this way, super-resolution applications have been carried out on the deep network model developed based on generative adversarial networks (GAN) by using three different datasets. The successful performance of the results was evaluated with PSNR (peak signal to noise ratio) and SSIM (structural similarity index measure). In addition, healthy - unhealthy classification application was carried out by means of a classifier network developed based on convolutional neural networks (CNN) to evaluate the super-resolution images obtained using different datasets. The obtained results show the importance of combining medical thermal imaging with super-resolution methods.

scholarly journals Mapping and monitoring of the land use/cover changes in the wider area of Itanos, Crete, using very high resolution EO imagery with specific interest in archaeological sites

2020 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Swaiti Suman
Keyword(s):  
High Resolution ◽  
Image Data ◽  
Archaeological Site ◽  
Archaeological Sites ◽  
Large Area ◽  
Open Spaces ◽  
Site Mapping ◽  
Object Based ◽  
Rule Sets ◽  
Rule Set

Archaeological site mapping is important for both understanding the history and protectingthe sites from excavation during developmental activities. As archaeological sites aregenerally spread over a large area, use of high spatial resolution remote sensing imageryis becoming increasingly applicable in the world. The main objective of this study is tomap the land cover of the Itanos area of Crete and of its changes, with specific focus onthe detection of the landscape’s archaeological features. Six satellite images were acquiredfrom the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digitalimagery of two known archaeological sites was used for validation. An object-based imageanalysis classification was subsequently developed using the five acquired satellite images.Two rule sets were created, one using the standard four bands which both satellites haveand another for the two WorldView-2 images with their four extra bands included. Validationof the thematic maps produced from the classification scenarios confirmed a differencein accuracy amongst the five images. Comparing the results of a 4-band rule set versusthe 8-band rule set showed a slight increase in classification accuracy using extra bands.The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separatingthe archaeological sites from the open spaces with little or no vegetation proved tobe challenging. This was mainly due to the high spectral similarity between rocks and thearchaeological ruins. The high resolution of the satellite data allowed for the accuracy indefining larger archaeological sites, but still there was difficulty in distinguishing smallerareas of interest. The digital image data provided a very good 3D representation for thearchaeological sites, assisting as well as in validating the satellite-derived classificationmaps. To conclude, our study provides further evidence that use of high resolution imagerymay allow for archaeological sites to be located, but only where the archaelogical featuresare of an adequate size.

scholarly journals Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target

2021 ◽  
Vol 11 (2) ◽  
pp. 582
Author(s):  
Zean Bu ◽  
Changku Sun ◽  
Peng Wang ◽  
Hang Dong
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Calibration Method ◽  
Multiple Sensors ◽  
Triangular Pyramid ◽  
World Coordinate System ◽  
Flash Lidar ◽  
Novel Method ◽  
3D Information ◽  
Incremental Validation

Calibration between multiple sensors is a fundamental procedure for data fusion. To address the problems of large errors and tedious operation, we present a novel method to conduct the calibration between light detection and ranging (LiDAR) and camera. We invent a calibration target, which is an arbitrary triangular pyramid with three chessboard patterns on its three planes. The target contains both 3D information and 2D information, which can be utilized to obtain intrinsic parameters of the camera and extrinsic parameters of the system. In the proposed method, the world coordinate system is established through the triangular pyramid. We extract the equations of triangular pyramid planes to find the relative transformation between two sensors. One capture of camera and LiDAR is sufficient for calibration, and errors are reduced by minimizing the distance between points and planes. Furthermore, the accuracy can be increased by more captures. We carried out experiments on simulated data with varying degrees of noise and numbers of frames. Finally, the calibration results were verified by real data through incremental validation and analyzing the root mean square error (RMSE), demonstrating that our calibration method is robust and provides state-of-the-art performance.

scholarly journals Integrating structure and function: mapping the hierarchical spatial heterogeneity of urban landscapes

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Yuguo Qian ◽  
Weiqi Zhou ◽  
Steward T. A. Pickett ◽  
Wenjuan Yu ◽  
Dingpeng Xiong ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Heterogeneity ◽  
Urban Ecosystem ◽  
Tree Canopy ◽  
Urban Landscapes ◽  
Shenzhen City ◽  
Object Based ◽  
Level 3 ◽  
Industrial Zones ◽  
Level 2

Abstract Background Cities are social-ecological systems characterized by remarkably high spatial and temporal heterogeneity, which are closely related to myriad urban problems. However, the tools to map and quantify this heterogeneity are lacking. We here developed a new three-level classification scheme, by considering ecosystem types (level 1), urban function zones (level 2), and land cover elements (level 3), to map and quantify the hierarchical spatial heterogeneity of urban landscapes. Methods We applied the scheme using an object-based approach for classification using very high spatial resolution imagery and a vector layer of building location and characteristics. We used a top-down classification procedure by conducting the classification in the order of ecosystem types, function zones, and land cover elements. The classification of the lower level was based on the results of the higher level. We used an object-based methodology to carry out the three-level classification. Results We found that the urban ecosystem type accounted for 45.3% of the land within the Shenzhen city administrative boundary. Within the urban ecosystem type, residential and industrial zones were the main zones, accounting for 38.4% and 33.8%, respectively. Tree canopy was the dominant element in Shenzhen city, accounting for 55.6% over all ecosystem types, which includes agricultural and forest. However, in the urban ecosystem type, the proportion of tree canopy was only 22.6% because most trees were distributed in the forest ecosystem type. The proportion of trees was 23.2% in industrial zones, 2.2% higher than that in residential zones. That information “hidden” in the usual statistical summaries scaled to the entire administrative unit of Shenzhen has great potential for improving urban management. Conclusions This paper has taken the theoretical understanding of urban spatial heterogeneity and used it to generate a classification scheme that exploits remotely sensed imagery, infrastructural data available at a municipal level, and object-based spatial analysis. For effective planning and management, the hierarchical levels of landscape classification (level 1), the analysis of use and cover by urban zones (level 2), and the fundamental elements of land cover (level 3), each exposes different respects relevant to city plans and management.

scholarly journals An Automatic Offset Calibration Method for Differential Charge-Based Capacitance Measurement

2021 ◽  
Vol 11 (2) ◽  
pp. 22
Author(s):  
Umberto Ferlito ◽  
Alfio Dario Grasso ◽  
Michele Vaiana ◽  
Giuseppe Bruno
Keyword(s):  
Standard Deviation ◽  
Output Voltage ◽  
Process Variations ◽  
Calibration Method ◽  
Capacitance Measurement ◽  
Effective Technique ◽  
Fully Integrated ◽  
Front End ◽  
Novel Method ◽  
On Chip

Charge-Based Capacitance Measurement (CBCM) technique is a simple but effective technique for measuring capacitance values down to the attofarad level. However, when adopted for fully on-chip implementation, this technique suffers output offset caused by mismatches and process variations. This paper introduces a novel method that compensates the offset of a fully integrated differential CBCM electronic front-end. After a detailed theoretical analysis of the differential CBCM topology, we present and discuss a modified architecture that compensates mismatches and increases robustness against mismatches and process variations. The proposed circuit has been simulated using a standard 130-nm technology and shows a sensitivity of 1.3 mV/aF and a 20× reduction of the standard deviation of the differential output voltage as compared to the traditional solution.

scholarly journals A Comparison of Two Sensory Panels Trained with Different Feedback Calibration Range Specifications via Sensory Description of Five Beers

Foods ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 534 ◽  
Author(s):  
Line Elgaard ◽  
Line A. Mielby ◽  
Helene Hopfer ◽  
Derek V. Byrne
Keyword(s):  
Average Distance ◽  
Calibration Method ◽  
The Self ◽  
Use Of Self ◽  
Fixed Target ◽  
Immediate Feedback ◽  
Leader Following ◽  
Novel Method ◽  
Standard Deviations ◽  
Sensory Profiles

Feedback on panel performance is traditionally provided by the panel leader, following an evaluation session. However, a novel method for providing immediate feedback to panelists was proposed, the Feedback Calibration Method (FCM). The aim of the current study was to compare the performance of two panels trained by using FCM with two different approaches for ranges calibration, namely self-calibrated and fixed ranges. Both panels were trained using FCM for nine one-hour sessions, followed by a sensory evaluation of five beer samples (in replicates). Results showed no difference in sample positioning in the sensory space by the two panels. Furthermore, the panels’ discriminability was also similar, while the self-calibrated panel had the highest repeatability. The results from the average distance from target and standard deviations showed that the self-calibrated panel had the lowest distance from target and standard deviation throughout all sessions. However, the decrease in average distance from target and standard deviations over training sessions was similar among panels, meaning that the increase in performance was similar. The fact that both panels had a similar increase in performance and yielded similar sensory profiles indicates that the choice of target value calibration method is unimportant. However, the use of self-calibrated ranges could introduce an issue with the progression of the target scores over session, which is why the fixed target ranges should be applied, if available.

scholarly journals Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 320
Author(s):  
Emilio Guirado ◽  
Javier Blanco-Sacristán ◽  
Emilio Rodríguez-Caballero ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Biotic Interactions ◽  
Relevant Information ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

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