چکیده مقاله
Hyperspectral imaging has emerged as a prominent technology for remotely sensing and analyzing complex landscapes However, accurately classifying hyperspectral images is a challenging task due to the high dimensionality and inherent spectral variability In this study, we propose a novel approach to enhance classification accuracy by integrating both spectral and spatial information The main objective of this research is to address the limitations of traditional approaches that rely solely on spectral information for classification To achieve this, we applied a two step approach First, we extracted the spectral features from the hyperspectral images using state of the art algorithms Then, we integrated spatial information by considering the contextual relationships between pixels through spatial filtering techniques This allowed us to capture more relevant information and enhance the classification performance This study highlights the importance of incorporating both spectral and spatial information for the accurate classification of hyperspectral images
کلیدواژهها
نویسندگان
Ayda Mirzazadeh
Bachelor of Computer Engineering, Rasht Azad University;
Abdorreza Hesam Mohseni
University Lecturer of Computer Engineering, University of Guilan, Guilan, Iran;
شیوه ارجاع
Mirzazadeh, Ayda and Hesam Mohseni, Abdorreza,1402,Classification of hyperspectral images by integrating spectral and spatial information,5th International Conference on Software Computing,Rudsar
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی محاسبات نرم16 اسفند 1402 · رودسر