Remote Sensing and GIS Application for Earth Observation on the base of the Neural Networks in Aerospace Image Classification

Abstract

development of the Neural Network which has to beUp-to-date curetted task of the present geoinformation determining the relation of appropriate classes to the system is the processing of the remote sensing data. Image object. analyses in point of mathematical view bases on the theory The number of parameters on spatial characteristic ofthe image recognition where it is necessary idn .increase due to involving parameters calculated by of the input data to the appropriate classas oi thentifictio horizontal, vertical and diagonal pixels taking part in Bes de with methods of fuzzy logic one of the advance process. As a result it can be find out an increase of a methods of solution the foregoing problem is wide volume of computation process. application of Neural Networks in the area m The main aim of those research activities is development Nof methodologi of hieh-rerformance classification of These methods have been taken an adequate instrument Deofpmethdlgy ohing therforaNe class ino which can be described regulation of classification without aerspeac images uiteN r nestwoksfo m od application of the high accuracy mathematical value as it o ect d accepted term for understanding like "small", "significant" objects.etc.~~~~~~~~~~~~Integration in GIS Neural Networks is the suitable instrument for effective encouragement of decision-making. There are two approaches of image recognition based on the spectral and spatial characteristics of the investigated GAS spataal data p 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

The role of artificial neural network and machine learning in utilizing spatial information

“…It is frequently utilized for data with a closefitting between input variables and output data. In this scenario, the dataset is labeled, which means that the algorithm recognizes the features explicitly and makes predictions or classifications based on them [17]. The procedure can control the links between the two logical variables as the training time continues, agreeing for us to expect the new outcome.…”

“…The neural networks have the advantages that all modules can operate in parallel, thereby boosting proficiency in problem-solving, particularly in the field of image processing. The approach of developing ANN with back-propagation error within the context of this research has been proposed as a solution to the challenge of creating decoding indicators based on the superposition of spatial and spectral properties of images from the Landsat TM satellite [17]. Artificial immune systems (AIS) integrate a priori knowledge with biological immune systems [18] adaptive characteristics to provide a potent alternative to currently available pattern recognition, modeling, design, and control techniques.…”