Up-to-date curetted task of the present geoinformation system is the processing of the remote sensing data. Image analyses in point of mathematical view bases on the theory ofthe image recognition where it is necessary identification of the input data to the appropriate classis of the objects. Beside with methods of fuzzy logic one of the advance methods of solution the foregoing problem is wide application of Neural Networks in the area. These methods have been taken an adequate instrument which can be described regulation of classification without application of the high accuracy mathematical value as it accepted term for understanding like "small", "significant" etc. There are two approaches of image recognition based on the spectral and spatial characteristics of the investigated class of objects. It is expedient the combination of the both approaches in order to increase an accuracy of recognition and classification of scanned area with complex relief (highlands, settlements, mixed forest, etc.). Each selected individual elements of landscape shall be fount out based on the image fragment analyses, their forms, colors, correlation, and heterogeneity. Neural Network allows assessing interrelation of each selected individual elements of landscape. One of the advantages of the Neural Network is that all elements have an ability to operate in parallel that is essentially increasing the efficiency of problem solution, especially in the area of the image processing.
Remote Sensing and GIS Application for Earth Observation on the base of the Neural Networks in Aerospace Image Classification
2007-06-01
974755 byte
Conference paper
Electronic Resource
English
Earth observation and remote sensing
TIBKAT | 12.1995 - 16.2000; damit Ersch. eingest.
Image Classification in Remote Sensing using Functional Link Neural Networks
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