Abstract: Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.
Abstract: In this research the Preparation of Land use map of
scanner LISS III satellite data, belonging to the IRS in the Aghche
region in Isfahan province, is studied carefully. For this purpose, the
IRS satellite images of August 2008 and various land preparation
uses in region including rangelands, irrigation farming, dry farming,
gardens and urban areas were separated and identified. Therefore, the
GPS and Erdas Imaging software were used and three methods of
Maximum Likelihood, Mahalanobis Distance and Minimum Distance
were analyzed. In each of these methods, matrix error and Kappa
index were calculated and accuracy of each method, based on
percentages: 53.13, 56.64 and 48.44, were obtained respectively.
Considering the low accuracy of these methods in separation of land
preparation use, the visual interpretation of the map was used.
Finally, regional visits of 150 points were noted at random and no
error was observed. It shows that the map prepared by visual
interpretation is in high accuracy. Although the probable errors due
to visual interpretation and geometric correction might happen but
the desired accuracy of the map which is more than 85 percent is
reliable.
Abstract: This study was conducted in Malaysia to discover how
meaning and appreciation were construed among 35 Form Five
students. Panofsky-s theory was employed to discover the levels of
reasoning among students when various types of posters were
displayed. The independent variables used were posters that carried
explicit and implicit meanings; the moderating variable was students-
visual literacy levels while the dependent variable was the implicit
interpretation level. One-way ANOVA was applied for the data
analysis. The data showed that before students were exposed to
Panofsky-s theory, there were differences in thinking between boys,
who did not think abstractly or implicit in comparison to girls. The
study showed that students- visual literacy in posters depended on the
use of visual texts and illustration. This paper discuss further on
posters with text only have a tendency to be too abstract as opposed
to posters with visuals plus text.
Abstract: In this research three methods of Maximum Likelihood, Mahalanobis Distance and Minimum Distance were analyzed in the Western part of Isfahan province in the Iran country. For this purpose, the IRS satellite images and various land preparation uses in region including rangelands, irrigation farming, dry farming, gardens and urban areas were separated and identified. In these methods, matrix error and Kappa index were calculated and accuracy of each method, based on percentages: 53.13, 56.64 and 48.44, were obtained respectively. Considering the low accuracy of these methods to separate land uses due to spread of the land uses, it-s suggested the visual interpretation of the map, to preparing the land use map in this region. The map prepared by visual interpretation is in high accuracy if it will be accompany with the visit of the region.