Classification of Ultra-high Resolution Images using Soft Computing

The main objective of this paper is to use a computational intelligence algorithm for preparing a mapping map that categorizes different patterns of identification of infected areas and changes in radiation pollution. In this paper, the use of the fuzzy inference system has been proposed to determine the degree of radiation contamination in the regions. The study uses ultra-high resolution spectrometry data to detect uranium. The research area includes well-known uranium deposits, including LambaPur-Peddagattu, Chitrial and Koppunuru. The high-resolution Spectrometry data collected for uranium exploration was used to estimate the average absorption rate in the air due to the distribution of females (potassium per cent and uranium and thorium per million) in these areas. Mamdanichr('39')s Fuzzy Inference System has also been used to determine the amount of radiation contamination in each region. The results showed that the efficiency of this method was 76% accurate for the detection of three levels of radiation contamination (no radiation contamination, low radiation exposure, medium radiation and high radiation pollution) and 89% for the overall identification of contaminated areas from non-polluted areas.