Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering i...Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.展开更多
In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensi...In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.展开更多
Vocabulary teaching is one aspect of language teaching that has not been given the attention it deserves until recent years. For a long period of time, vocabulary is simply taught in the way by asking students to stud...Vocabulary teaching is one aspect of language teaching that has not been given the attention it deserves until recent years. For a long period of time, vocabulary is simply taught in the way by asking students to study and memorize its meaning and spelling, its part of speech and its general function in a sentence. Thus, a student with a command of five thousand English vocabulary still finds it hard to adapt himself to the requirement of our demanding reading assignments, in particular, to the extensive reading task, which is more demanding due to its wide range of materials and large amount of vocabularies. According to Wilkins (1979: 111) "Without grammar very little can be conveyed, without vocabulary, nothing can be conveyed." Yet without a deeper understanding of how vocabulary is taught in the classroom and which methods of teaching are more effective for learners, the teaching of vocabulary may not achieve the desired effects. By researching the topic on vocabulary learning and instruction, this essay intends to bring the attention of both teachers and learners to the weaknesses of the traditional approach of teaching vocabulary and some different strategies in vocabulary instruction with the aim of improving the students’ reading comprehension.\;展开更多
Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate loca...Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.展开更多
基金supported by the National Key Laboratory of ATR(9140C8002010706).
文摘Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.
基金Project(2007CB714407) supported by the Major State Basic Research and Development Program of ChinaProject(2004DFA06300) supported by Key International Collaboration Project in Science and TechnologyProjects(40571107, 40701102) supported by the National Natural Science Foundation of China
文摘In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.
文摘Vocabulary teaching is one aspect of language teaching that has not been given the attention it deserves until recent years. For a long period of time, vocabulary is simply taught in the way by asking students to study and memorize its meaning and spelling, its part of speech and its general function in a sentence. Thus, a student with a command of five thousand English vocabulary still finds it hard to adapt himself to the requirement of our demanding reading assignments, in particular, to the extensive reading task, which is more demanding due to its wide range of materials and large amount of vocabularies. According to Wilkins (1979: 111) "Without grammar very little can be conveyed, without vocabulary, nothing can be conveyed." Yet without a deeper understanding of how vocabulary is taught in the classroom and which methods of teaching are more effective for learners, the teaching of vocabulary may not achieve the desired effects. By researching the topic on vocabulary learning and instruction, this essay intends to bring the attention of both teachers and learners to the weaknesses of the traditional approach of teaching vocabulary and some different strategies in vocabulary instruction with the aim of improving the students’ reading comprehension.\;
基金Project(2020A1515010718)supported by the Basic and Applied Basic Research Foundation of Guangdong Province,China。
文摘Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.
