Wide swath Synthetic Aperture Radar (SAR) images acquired over sea areas contain a variety of information regarding small scale and mesoscale phenomena in the ocean and marine boundary layer e.g. spills, slicks, surfa...Wide swath Synthetic Aperture Radar (SAR) images acquired over sea areas contain a variety of information regarding small scale and mesoscale phenomena in the ocean and marine boundary layer e.g. spills, slicks, surface or internal waves, eddies, oceanic fronts. One of most challenging processing step is to create image objects describing these phenomena on SAR images. The most significant problem in the wide swath images is the backscattering trend at the range direction, which results a progressive brightness reduction over images from near to far range. This reduction affects the detection and classification of sea surface features on wide swath SAR images and a normalization step is needed in a certain incidence angle for compensating the brightness reduction. The aim of the present paper is to investigate the result of image normalization to a set of Wide Swath Mode SAR images. Dark areas were initially detected in SAR images using thresholds, adapted or not. Afterwards, SAR images were normalized and a global threshold was calculated for each image. Images were segmented and objects were created for each dark area. The results were compared to a reference dataset created from theoretical modeled values and extracted in a GIS environment. Results clearly indicate that overall accuracy of the detected dark areas has been increased after normalization. On the contrary, local thresholds were insufficient in producing acceptable results. The proposed normalization can be used as a pre-processing step in image classification.展开更多
Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
文摘Wide swath Synthetic Aperture Radar (SAR) images acquired over sea areas contain a variety of information regarding small scale and mesoscale phenomena in the ocean and marine boundary layer e.g. spills, slicks, surface or internal waves, eddies, oceanic fronts. One of most challenging processing step is to create image objects describing these phenomena on SAR images. The most significant problem in the wide swath images is the backscattering trend at the range direction, which results a progressive brightness reduction over images from near to far range. This reduction affects the detection and classification of sea surface features on wide swath SAR images and a normalization step is needed in a certain incidence angle for compensating the brightness reduction. The aim of the present paper is to investigate the result of image normalization to a set of Wide Swath Mode SAR images. Dark areas were initially detected in SAR images using thresholds, adapted or not. Afterwards, SAR images were normalized and a global threshold was calculated for each image. Images were segmented and objects were created for each dark area. The results were compared to a reference dataset created from theoretical modeled values and extracted in a GIS environment. Results clearly indicate that overall accuracy of the detected dark areas has been increased after normalization. On the contrary, local thresholds were insufficient in producing acceptable results. The proposed normalization can be used as a pre-processing step in image classification.
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
