Abstract
Aerial and satellite multispectral images are important source of intelligence information. However, the object classification accuracy in those images for reasons such as camouflage, use of decoys, and others often turns out to be insufficient. The objective of the study is to develop a method for computer-aided analysis of aerial and satellite multispectral images, which allows improving classification accuracy. This objective is achieved by incorporating geospatial information (topographic, geodetic, about land cover types) into the classification process. As a mathematical basis of the method is used subjective logic of A. Jøsang. The effectiveness of the proposed method has been demonstrated by computer modeling using ArcGIS ModelBuilder tools.
Highlights
It is well known that one of the richest sources of data on the terrain, landscape, operational environment, natural and man-made objects are aerial and satellite images [1]
If the external conditions that existed at the moment when the image was formed are known, at this step, a posteriori image processing can be carried out
The proposed approach is based on the assumption that it is possible to improve the accuracy of object classification in aerial and satellite images by incorporating geospatial information into the classification process
Summary
It is well known that one of the richest sources of data on the terrain, landscape, operational environment, natural and man-made objects are aerial and satellite images [1]. The acquisition of such images and their analysis are the tasks of Imagery Intelligence activity [2]. Analysis of the digital multispectral images is a cognitive process and it is performed by an operator-interpreter (O-I) [7]. The use of such systems allows to free the O-I from a significant part of the routine work, but all the work related to the semantic analysis (understanding) of the ground scene, including the classification of objects and the decisionmaking, are performed by the O-I
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