Analysis of Visual Interpretation of Satellite Data

Abstract

ABSTRACT:Millions of people of all ages and expertise are using satellite and aerial data as an important input for their work in many different fields. Satellite data are also gradually finding a new place in education, especially in the fields of geography and in environmental issues. The article presents the results of an extensive research in the area of visual interpretation of image data carried out in the years 2013 -2015 in the Czech Republic. The research was aimed at comparing the success rate of the interpretation of satellite data in relation to a) the substrates (to the selected colourfulness, the type of depicted landscape or special elements in the landscape) and b) to selected characteristics of users (expertise, gender, age). The results of the research showed that (1) false colour images have a slightly higher percentage of successful interpretation than natural colour images, (2) colourfulness of an element expected or rehearsed by the user (regardless of the real natural colour) increases the success rate of identifying the element (3) experts are faster in interpreting visual data than non-experts, with the same degree of accuracy of solving the task, and (4) men and women are equally successful in the interpretation of visual image data.

Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach

“…It is widely known that appropriate false color composite imagery can provide a great amount of information for the interpreter as it can considerably improve visual perception. A study by Svatonova [124] showed that non-expert image interpreters can interpret false color satellite imagery very well. Specifically, Svatonova [124] recommended providing false color imagery alongside true color imagery in web mapping services to enhance the image interpretation performance of the non-experts.…”

“…A study by Svatonova [124] showed that non-expert image interpreters can interpret false color satellite imagery very well. Specifically, Svatonova [124] recommended providing false color imagery alongside true color imagery in web mapping services to enhance the image interpretation performance of the non-experts. Hence, to collect spatial data (i.e., tree locations) and attribute data (i.e., tree basic geometrical property) about ITCs through crowdsourcing approach (i.e., tree identification, ITC identification) in this study, true color R-G-B and false color NIR1-R-G composites of the aforementioned VW3 imagery were produced for the test plot and provided to the volunteers through the developed platform.…”

“…Human factors play a critical role in visual interpretation of remotely sensed imagery [178]. The different characteristics of the image interpreter influence the performance of object recognition and classification and the measurement of qualitative and quantitative attributes of the identified object [124]. Therefore, visual interpretation is a subjective process [144,145], and the interpretation results of remote mapping tasks are variable among the different volunteers.…”

“…Therefore, visual interpretation is a subjective process [144,145], and the interpretation results of remote mapping tasks are variable among the different volunteers. Previous studies have demonstrated that the with human factors such as experience, expertise, education, motivations and willingness to obtain good results, short-term visual working memory capacity, age, gender, mental conditions, and fatigue may be correlated with the performance of visual interpretation of the imagery [124,145,179]. Contextual information, monocular cues, and image characteristics such as onsite knowledge from the remotely sensed environment, prior knowledge about the expected object types in the scene, texture, shadow, tone or color, size, shape, pattern, location, association, aspect, and spatial resolution are the other factors that may affect the visual interpretation performance [124,[179][180][181][182][183][184].…”

“…Contextual information, monocular cues, and image characteristics such as onsite knowledge from the remotely sensed environment, prior knowledge about the expected object types in the scene, texture, shadow, tone or color, size, shape, pattern, location, association, aspect, and spatial resolution are the other factors that may affect the visual interpretation performance [124,[179][180][181][182][183][184]. Previous studies showed that the visual interpretation performance may be influenced by the feature type and the degree of the task complexity [124]. Furthermore, the external factors such as the working environment conditions and screen quality are the other impacting factors on the process of image interpretation [145].…”