An Efficient Classification of Hyperspectral Remotely Sensed Data Using Support Vector Machine

• Autorzy: Mahendra H. N. , Mallikarjunaswamy S.

Identyfikatory

DOI

10.24425/ijet.2022.141280

• Języki publikacji: EN

Abstrakty

EN

This work present an efficient hardware architecture of Support Vector Machine (SVM) for the classification of Hyperspectral remotely sensed data using High Level Synthesis (HLS) method. The high classification time and power consumption in traditional classification of remotely sensed data is the main motivation for this work. Therefore presented work helps to classify the remotely sensed data in real-time and to take immediate action during the natural disaster. An embedded based SVM is designed and implemented on Zynq SoC for classification of hyperspectral images. The data set of remotely sensed data are tested on different platforms and the performance is compared with existing works. Novelty in our proposed work is extend the HLS based FPGA implantation to the onboard classification system in remote sensing. The experimental results for selected data set from different class shows that our architecture on Zynq 7000 implementation generates a delay of 11.26 μs and power consumption of 1.7 Watts, which is extremely better as compared to other Field Programmable Gate Array (FPGA) implementation using Hardware description Language (HDL) and Central Processing Unit (CPU) implementation.

Słowa kluczowe

EN

support vector machine; SVM; central processing unit; CPU; digital signal processor; DSP; field programmable gate array; FPGA; high level synthesis; HLS; hardware description language; HDL

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2022
• Tom: Vol. 68. No. 3
• Strony: 609--617
• Opis fizyczny: Bibliogr. 38 poz., schem., tab.

Twórcy

autor

Mahendra H. N.

• Department of Electronics and Communication Engineering, JSS Academy of Technical Education Bengaluru and Affiliated to Visvesvaraya Technological University, Belagavi, India

autor

Mallikarjunaswamy S.

• Department of Electronics and Communication Engineering, JSS Academy of Technical Education Bengaluru and Affiliated to Visvesvaraya Technological University, Belagavi, India

Bibliografia

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