Fusion of Infrared Thermal Image and Visible Image for 3D Thermal Model Reconstruction Using Smartphone Sensors

Yang, Ming‐Der; Su, Tung-Ching; Lin, Hung‐Yu

doi:10.3390/s18072003

Cited by 42 publications

(28 citation statements)

References 44 publications

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“…In addition, there is an additional scaling factor (S x , S y ) (unitless) as a result of the different lenses' FOV and the corresponding sensors' spatial resolution (pixel quantity and size). Such parameters can be calculated a priori using a one-time calibration protocol based on the methodology described in [13]. Fifty pair of IRT and RGB images acquired with θ ≈ 60 • and d object ≈ 3 m over the potato crop, and including the AWRS in the scene, were utilized for calibration.…”

Section: Image Acquisition and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Development of an Open-Source Thermal Image Processing Software for Improving Irrigation Management in Potato Crops (Solanum tuberosum L.)

Cucho‐Padin

Rinza

Ninanya

et al. 2020

Sensors

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Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( r P e a r s o n = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha - 1 ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m 3 ha - 1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings.

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Section: Image Acquisition and Analysismentioning

confidence: 99%

“…On the basis of the black body radiation theory, an infrared thermography (IRT) camera can detect the spectral radiance emitted by an object due to its temperature [13][14][15]. Such a radiance, which ranges from 7 to 14 µm, is converted into electrical signals and then displayed as a 2D array or IRT image.…”

Section: Introductionmentioning

confidence: 99%

Development of an Open-Source Thermal Image Processing Software for Improving Irrigation Management in Potato Crops (Solanum tuberosum L.)

Cucho‐Padin

Rinza

Ninanya

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Yang et al [21] fused RGB images and thermal images to reconstruct 3D thermal models using two smartphones. All of the used sensors (two smartphone RGB sensors and one FLIR low-cost thermal sensor) are installed on a theodolite.…”

Section: Vidas and Moghadammentioning

confidence: 99%

Evaluating Thermal Attribute Mapping Strategies for Oblique Airborne Photogrammetric System AOS-Tx8

et al. 2019

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Thermal imagery is widely used in various fields of remote sensing. In this study, a novel processing scheme is developed to process the data acquired by the oblique airborne photogrammetric system AOS-Tx8 consisting of four thermal cameras and four RGB cameras with the goal of large-scale area thermal attribute mapping. In order to merge 3D RGB data and 3D thermal data, registration is conducted in four steps: First, thermal and RGB point clouds are generated independently by applying structure from motion (SfM) photogrammetry to both the thermal and RGB imagery. Next, a coarse point cloud registration is performed by the support of georeferencing data (global positioning system, GPS). Subsequently, a fine point cloud registration is conducted by octree-based iterative closest point (ICP). Finally, three different texture mapping strategies are compared. Experimental results showed that the global image pose refinement outperforms the other two strategies at registration accuracy between thermal imagery and RGB point cloud. Potential building thermal leakages in large areas can be fast detected in the generated texture mapping results. Furthermore, a combination of the proposed workflow and the oblique airborne system allows for a detailed thermal analysis of building roofs and facades.

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“…Schnebele and Cervone combined volunteered geographic information (VGI) with high-resolution remote sensing data, and the resultant modified contour regions dramatically assisted in the presentation of detailed flood hazard maps [15]. In addition to employing smartphone sensors in professional fields, such as construction inspection [16], many people use smartphone cameras to record their living environments through captured images and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter, so that crowdsourcing data regarding various phenomena can be inexpensively acquired. Kaplan and Haenlein classified social media applications subsumed into specific categories by characteristic, including collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds [17].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

et al. 2020

Self Cite

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Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

show abstract

Fusion of Infrared Thermal Image and Visible Image for 3D Thermal Model Reconstruction Using Smartphone Sensors

Cited by 42 publications

References 44 publications

Development of an Open-Source Thermal Image Processing Software for Improving Irrigation Management in Potato Crops (Solanum tuberosum L.)

Development of an Open-Source Thermal Image Processing Software for Improving Irrigation Management in Potato Crops (Solanum tuberosum L.)

Evaluating Thermal Attribute Mapping Strategies for Oblique Airborne Photogrammetric System AOS-Tx8

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

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