A leading text for undergraduate- and graduate-level courses, this book introduces widely used forms of remote sensing imagery and their applications in plant sciences, hydrology, earth sciences, and land use analysis. The text provides comprehensive coverage of principal topics and serves as a framework for organizing the vast amount of remote sensing information available on the Web. Including case studies and review questions, the book's four sections and 21 chapters are carefully designed as independent units that instructors can select from as needed for their courses.
Advances in sensor technology are revolutionizing the way remotely sensed data are
collected, managed, and analyzed. The incorporation of latest-generation sensors to
airborne and satellite platforms is currently producing a nearly continual stream of
high-dimensional data, and this explosion in the amount of collected information
has rapidly created new processing challenges.
Signal processing has been playing an increasingly important role in remote sensing,
though most remote sensing literatures are concerned with remote sensing images. Many
data received by remote sensors such as microwave and geophysical sensors, are signals or
waveforms, which can be processed by analog and digital signal processing techniques.
Nowadays it is hard to find areas of human activity and development that have not
profited from or contributed to remote sensing. Natural, physical and social activities
find in remote sensing a common ground for interaction and development. From the
end-user point of view, Earth science, geography, planning, resource management,
public policy design, environmental studies, and health, are some of the areas whose
recent development has been triggered and motivated by remote sensing.
Nowadays it is hard to find areas of human activity and development that have not profited from or contributed to remote sensing. Natural, physical and social activities find in remote sensing a common ground for interaction and development. This book intends to show the reader how remote sensing impacts other areas of science, technology, and human activity, by displaying a selected number of high quality contributions dealing with different remote sensing applications.
Number Sense and Numeration, Grades 4 to 6 is a practical guide, in six volumes, that teachers will
find useful in helping students to achieve the curriculum expectations outlined for Grades 4 to 6
in the Number Sense and Numeration strand of The Ontario Curriculum, Grades 1–8: Mathematics,
2005. This guide provides teachers with practical applications of the principles and theories that
are elaborated in A Guide to Effective Instruction in Mathematics, Kindergarten to Grade 6, 2006.
In this research, remote sensing technology was used to evaluate urban development and its thermal characteristics through mapping impervious surfaces and evaluating thermal infrared images. The study is carried out in the northern part of Ho Chi Minh City, which is experienced an accelerated urban development since the end of 1980s. Landsat and Aster images were used to calculate the variation in urban impervious surfaces from 1989 to 2006. Thermal bands were processed to obtain land surface temperatures for investigating the urban heat island effect...
Bốn hỗn hợp vi khuẩn thể hiện đặc tính phân hủy quorum sensing và đối kháng Vibrio spp. ở điều kiện in vitro đã được thử nghiệm ở qui mô pilot trên ấu trùng tôm sú và cá chẽm. Kết quả các thí nghiệm trên ấu trùng cá chẽm cho thấy hỗn hợp của hai chủng vi khuẩn Ch102 và Ch104 thể hiện khả năng nâng cao tỉ lệ sống của ấu trùng cá chẽm 30 ngày tuổi so với nghiệm thức không bổ sung vi khuẩn (tỉ lệ sống ấu trùng cá chẽm ở nghiệm thức bổ sung hỗn...
This monograph discusses U.S. Air Force progress toward implementing sense and respond logistics or, as defined more broadly, sense and respond combat support. It describes some of the research that has been conducted on the military combat support system, focusing on improvements in prediction capability,
In predicate-argument structure analysis, it is important to capture non-local dependencies among arguments and interdependencies between the sense of a predicate and the semantic roles of its arguments. However, no existing approach explicitly handles both non-local dependencies and semantic dependencies between predicates and arguments.
We present a preliminary study on unsupervised preposition sense disambiguation (PSD), comparing different models and training techniques (EM, MAP-EM with L0 norm, Bayesian inference using Gibbs sampling). To our knowledge, this is the ﬁrst attempt at unsupervised preposition sense disambiguation.
This paper focuses on domain-speciﬁc senses and presents a method for assigning category/domain label to each sense of words in a dictionary. The method ﬁrst identiﬁes each sense of a word in the dictionary to its corresponding category. We used a text classiﬁcation technique to select appropriate senses for each domain. Then, senses were scored by computing the rank scores. We used Markov Random Walk (MRW) model. The method was tested on English and Japanese resources, WordNet 3.0 and EDR Japanese dictionary. ...
We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as “but” or “because”. We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguistically informed features, including polarity tags, Levin verb classes, length of verb phrases, modality, context, and lexical features. ...
To date, parsers have made limited use of semantic information, but there is evidence to suggest that semantic features can enhance parse disambiguation. This paper shows that semantic classes help to obtain signiﬁcant improvement in both parsing and PP attachment tasks. We devise a gold-standard sense- and parse tree-annotated dataset based on the intersection of the Penn Treebank and SemCor, and experiment with different approaches to both semantic representation and disambiguation. For the Bikel parser, we achieved a maximal error reduction rate over the baseline parser of 6.
Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguation. In this paper, we present a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the Oxford Dictionary of English. We assess the quality of the mapping and the induced clustering, and evaluate the performance of coarse WSD systems in the Senseval-3 English all-words task.
Word Sense Disambiguation suffers from a long-standing problem of knowledge acquisition bottleneck. Although state of the art supervised systems report good accuracies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an approach for learning coarser and more general set of concepts from a sense tagged corpus, in order to alleviate the knowledge acquisition bottleneck.
This paper describes automatic techniques for mapping 9611 entries in a database of English verbs to WordNet senses. The verbs were initially grouped into 491 classes based on syntactic features. Mapping these verbs into WordNet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval.