aspg-3-2464-1791417730 20261008000210 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 14 2 Saadya Fahad Jabbar College of education – Ibn rushed for human science, University of Baghdad, Baghdad, Iraq Nuha Sami Mohsin College of education – Ibn rushed for human science, University of Baghdad, Baghdad, Iraq Bourair Al-Attar bourair.alattar@alameed.edu.iq Israa Ibraheem Al_Barazanchi Department of Communication Technology Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq The combination of deep neural networks and assistance vector machines for hyperspectral image recognition is presented in this work. A key issue in the real-world hyperspectral imaging system is hyperspectral picture recognition. Although deep learning can replicate highly dimensional feature vectors from source data, it comes at a high cost in terms of time and the Hugh phenomenon. The selection of the kernel feature and limit has a significant impact on the presentation of a kernel-based learning system. We introduce Support Vector Machine (SVM), a kernel learning method that is used to feature vectors obtained from deep learning on hyperspectral images. By modifying the data structure's parameters and kernel functions, the learning system's ability to solve challenging problems is enhanced. The suggested approaches' viability is confirmed by the outcomes of the experiments. At a particular rate, accuracy of testing for classification is around 90%. Moreover, to significantly make framework robust, validation is done using 5-flod verification. 2024 219 226 2464 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.140218 https://www.americaspg.com/journal/3/article/2464 https://www.americaspg.com/storage/71706724516.pdf