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Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A study focusing on the classification of surficial materials in the Umiujalik Lake area using multisource data including polarimetric SAR data, Landsat optical data, and a digital elevation model (DEM) has been conducted. The purpose of this study is to explore the classification performance by comparing different feature combinations and different classifiers. First, 4 classification methods were compared on different combinations of features of intensity and texture. Second, the effects of dimension reduction algorithms for classification were investigated. Finally, 6 different dimension reduction methods were used to see if classification performance can be improved or remain stable by using fewer dimensions. Results show that adding texture features can help improve classification accuracy; the best classification accuracy is achieved by the rotation forest classification method using the combination of intensity and texture features; the classification performance remains stable using fewer features. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although computer simulations indicate that mitosis may be important to the mechanics of morphogenetic movements, algorithms to identify mitoses in bright field images of embryonic epithelia have not previously been available. Here, the authors present an algorithm that identifies mitoses and their orientations based on the motion field between successive images. Within this motion field, the algorithm seeks 'mitosis motion field prototypes' characterised by convergent motion in one direction and divergent motion in the orthogonal direction, the local motions produced by the division process. The algorithm uses image processing, vector field analyses and pattern recognition to identify occurrences of this prototype and to determine its orientation. When applied to time-lapse images of gastrulation and neurulation-stage amphibian (Ambystoma mexicanum) embryos, the algorithm achieves identification accuracies of 68 and 67%, respectively and angular accuracies of the order of 30°, values sufficient to assess the role of mitosis in these developmental processes. [ABSTRACT FROM AUTHOR]

Recent computational and analytical studies have shown that cellular fabric - as embodied by average cell size, aspect ratio and orientation - is a key indicator of the stresses acting in an embryonic epithelium. Cellular fabric in real embryonic tissues could not previously be measured automatically because the cell boundaries tend to be poorly defined, significant lighting and cell pigmentation differences occur and tissues contain a variety of cell geometries. To overcome these difficulties, four algorithms were developed: least squares ellipse fitting (LSEF), area moments (AM), correlation and axes search (CAS) and Gabor filters (GF). The AM method was found to be the most reliable of these methods, giving typical cell size, aspect ratio and orientation errors of 18%, 0.10 and 7.4°, respectively, when evaluated against manually segmented images. The power of the AM algorithm to provide new insights into the mechanics of morphogenesis is demonstrated through a brief investigation of gastrulation, where fabric data suggest that key gastrulation movements are driven by epidermal tensions circumferential to the blastopore. [ABSTRACT FROM AUTHOR]