Evaluation of Ocean Color Scanner (OCS) photographic and digital data: Santa Barbara Channel test site, 29 October 1975 overflight

A summary of Ocean Color Scanner data was examined to evaluate detection and discrimination capabilities of the system for marine resources, oil pollution and man-made sea surface targets of opportunity in the Santa Barbara Channel. Assessment of the utility of OCS for the determination of sediment transport patterns along the coastal zone was a secondary goal. Data products provided 1975 overflight were in digital and analog formats. In evaluating the OCS data, automated and manual procedures were employed. A total of four channels of data in digital format were analyzed, as well as three channels of color combined imagery, and four channels of black and white imagery. In addition, 1:120,000 scale color infrared imagery acquired simultaneously with the OCS data were provided for comparative analysis purposes

Multispectral determination of soil moisture

The edited Guymon soil moisture data collected on August 2, 5, 14, 17, 1978 were grouped into four field cover types for statistical analysis. These are the bare, milo with rows parallel to field of view, milo with rows perpendicular to field of view and alfalfa cover groups. There are 37, 22, 24 and 14 observations respectively in each group for each sensor channel and each soil moisture layer. A subset of these data called the 'five cover set' (VEG5) limited the scatterometer data to the 15 deg look angle and was used to determine discriminant functions and combined group regressions

Landuse Analysis Using BASIC+ Interactive Image Processing for Teaching: A Comparison with LARSYS

Landuse analyses continue to be the medium for communicating important spatial, spectral, and temporal remote sensing concepts. Unfortunately, hardware and software constraints often limit these activities to the examination of photographic formats of remotely sensed data. Such constraints cause manual interpretation techniques to be given inordinate attention compared to digital image processing. Even when the instructor is actively involved in image processing research, batch-mode processing may dominate. Again the student is confronted with hard-copy, this time in the form of computer printouts. A more effective remote sensing education is realized if students have the opportunity to experience interactive digital image processing. To this end, BASIC+ digital image processing has been implemented at the University of California, Santa Barbara as an integral part of the Geography Department\u27s remote sensing curriculum. Students interrogate Landsat images to extract digital number (DN) values, experiment with their own preprocessing algorithms and use Boolean logic classification. Analyses are performed on a 15 x 45km coastal study area encompassing a diversity of landuses and discrimination problems. This paper first summarizes the image processing system configuration including: 1 ) Data Acquisition -SUBIMG. (select subimage from scene) 2) Preprocessing -PREPRO. (arithmetic operations, eg. ratio) -SHIFT. (edge enhancement) 3) Class Specific Processing -PTRAIN. (train on preprocessed file) -TRAIN. (train on unpreprocessed file) -TEST. (select test data) 4) Data Analysis -STATS. (parametric statistics) -HISTO. (histogram) -DIVER. (divergence) 5) Classification -PTHEME. (Boolean on preprocessed files) -THEME. (Boolean on unpreprocessed files) 6) Utility -FIXIT. (list DN values; file clean-up) -LOOKC. (grey map) The paper concludes with the results of the student landuse classification experience in the 115B remote sensing class (the second of a three course sequence). Evaluations include: 1) Student reactions and perceptions a. Innovative preprocessing to optimize classification b. The \u27real\u27 utility of remote sensing 2) Comparison of student results with LARSYS classifications using both student and researcher training and test data. Overall reactions by students and faculty indicate that the BASIC+ image processing system is effective for both educational and research purposes

Prior-informed distant supervision for temporal evidence classification

Temporal evidence classification, i.e., finding associations between temporal expressions and relations expressed in text, is an important part of temporal relation extraction. To capture the variations found in this setting, we employ a distant supervision approach, modeling the task as multi-class text classification. There are two main challenges with distant supervision: (1) noise generated by incorrect heuristic labeling, and (2) distribution mismatch between the target and distant supervision examples. We are particularly interested in addressing the second problem and propose a sampling approach to handle the distribution mismatch. Our prior-informed distant supervision approach improves over basic distant supervision and outperforms a purely supervised approach when evaluated on TAC-KBP data, both on classification and end-to-end metrics

Class-Based Language Modeling for Translating into Morphologically Rich Languages

Class-based language modeling (LM) is a long-studied and effective approach to overcome data sparsity in the context of n-gram model training. In statistical machine translation (SMT), differ- ent forms of class-based LMs have been shown to improve baseline translation quality when used in combination with standard word-level LMs but no published work has systematically com- pared different kinds of classes, model forms and LM combination methods in a unified SMT setting. This paper aims to fill these gaps by focusing on the challenging problem of translating into Russian, a language with rich inflectional morphology and complex agreement phenomena. We conduct our evaluation in a large-data scenario and report statistically significant BLEU im- provements of up to 0.6 points when using a refined variant of the class-based model originally proposed by Brown et al. (1992)