IMPACTS OF SENSOR SPATIAL
RESOLUTION ON REMOTE SENSING
IMAGE CLASSIFICATION
Thomas U. Omali
Department of Geoinformatics and Surveying
University of Nigeria Enugu Campus
Enugu, Nigeria
E-mail: t.omali@yahoo.com
GSJ: Volume 6, Issue 1, January 2018, pp. 63-68, Online: ISSN 2320-9186
ABSTRACT
There has been a significant upsurge in the development of satellite platforms with enormous number of sensors in recent years. Several remotely sensed data with spatial resolutions extending from 0.5 to 25,000m are accessible for diverse applications. This advancements result in novel and substantial modifications as well as challenges in the methodology of remotely sensed data analysis, integration, and the efficient spatial modelling of these data. This paper critically reviews the impacts of sensor spatial resolution on remote sensing image classification. First and foremost, an introductory background was presented. Second, spatial resolution was characterized in terms of four classes including low, medium, high, and very high resolutions. Third, basic perception on sensor spatial resolution was discussed. The fourth session dealt with sensor spatial resolution and mixed pixel challenge. And the fifth session elaborated the suitability of specific spatial resolution for image classification. Finally, it was revealed that, even though, higher spatial resolution remotely sensed data may deliver improved data, it may not always be desired due to intricate nature of interpretation, data volume and data acquisition costs. And despite the increasing propensity for more satellites with improved spatial characteristics and to develop applications for the same, the lower spatial resolution satellites remain invaluable.
Keywords Classification, Imagery, Mixels, Pixel, Remote sensing, Sensor, and Spatial resolution.
6.0 Conclusion
There has been upsurge in the development of satellite platforms with enormous number of sensors in recent years. Several remotely sensed data with spatial resolutions ranging from 0.5 to 25,000m are accessible for diverse applications. This advancements result in novel and substantial modifications as well as challenges in the methodology of remotely sensed data analysis, integration, and the efficient spatial modelling of these data. This discussed the impacts of sensor spatial resolution on remote sensing image classification. Spatial resolution may be broadly categorized into four including low, medium, high, and very high resolutions. These characterizations depend on the amount of the surface feature or surface detail that a satellite sensor can discriminate and/or the Ground Sampling Distance. Choosing suitable spatial resolution(s) is obviously one of the basic factors when employing remotely sensed data for land use and land cover classification and mapping. This is because the choice of different spatial resolution can lead to misleading interpretation since it affects classification details and accuracy. One can easily understand spatial resolution in terms of the feature to be sensed. This may be achieved through high- and low- resolution scene models based on the relationships between the sizes of the scene elements and the resolution cell of the sensor. Of course, the size of ground features relative to the spatial resolution of a sensor is directly related to image variance. Mixed Pixels is a major challenge in image classification especially when per-pixel classifiers are used. It is manifested depending mostly on sensor spatial resolution and the spatial variability of the observed surface. Each pixel within an image provides a single measurement for an area that comprises multiple components called endmembers. Mixed pixels are very difficult to discriminate from each other. Obviously, certain feature might not be recognized at a low or medium resolution, but with a finer spatial resolution, more details about objects in a scene become available. The suitability of specific spatial resolution for image classification depends on certain factors including the type of environment, the kind of information desired and the techniques used to extract information. Thus, selecting a specific spatial resolution from various spatial resolutions for particular purpose can lead to ambiguous image interpretation. Finally, it was revealed that, even though, higher spatial resolution remotely sensed data may deliver improved data, it may not always be desired due to intricate nature of interpretation, data volume and data acquisition costs. And despite the increasing propensity for more satellites with improved spatial characteristics and to develop applications for the same, the lower spatial resolution satellites remain invaluable. Selecting suitable spatial resolution(s) of remotely sensed data is one of the most essential considerations prior to remotely sensed data classification. When defining the appropriate spatial resolution for analysis, certain factors are very salient including spatial resolution of available data, environmental conditions, anticipated information, and procedures employed in extracting information. Proper image classification is to a great extent dependent on the knowledge of certain spatial attributes of the data so as to determine the appropriate classification procedure and parameters to use. The reason for this is because the spatial resolution of remotely sensed data largely affects each of the stages involve in image classification. With the increased availability of very high resolution multi-spectral images spatial resolution variation will play an increasingly important role in the employment of remotely sensed imagery. Though, higher spatial resolution remotely sensed data may deliver enhanced data, it may not always be desired due to intricate nature of interpretation, data volume and data acquisition costs. And despite the increasing propensity for more satellites with improved spatial characteristics and to develop applications for the same, the lower spatial resolution satellites remain invaluable.
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