- Tytuł:
- An Efficient Classification of Hyperspectral Remotely Sensed Data Using Support Vector Machine
- Autorzy:
-
Mahendra, H. N.
Mallikarjunaswamy, S. - Powiązania:
- https://bibliotekanauki.pl/articles/2134051.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
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 - Opis:
- 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.
- Źródło:
-
International Journal of Electronics and Telecommunications; 2022, 68, 3; 609--617
2300-1933 - Pojawia się w:
- International Journal of Electronics and Telecommunications
- Dostawca treści:
- Biblioteka Nauki
Artykuł