Next Article in Journal
The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes
Previous Article in Journal / Special Issue
High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting
Article

An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data

1
Department of Information Technology, Ling Tung University, Taichung 40851, Taiwan
2
GIS Research Center, Director, Feng Chia University, Taichung 40724, Taiwan
3
GIS Research Center, Feng Chia University, Taichung 40724, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Eric Kergosien
ISPRS Int. J. Geo-Inf. 2021, 10(4), 242; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040242
Received: 22 January 2021 / Revised: 4 April 2021 / Accepted: 6 April 2021 / Published: 7 April 2021
Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation. However, most of the research has currently focused on the classification of pixel-based image data for analysis. The study was carried out to develop a multi-category crop hyperspectral image classification system to identify the major crops in the Chiayi Golden Corridor. The hyperspectral image data from CASI (Compact Airborne Spectrographic Imager) were used as the experimental data in this study. A two-stage classification was designed to display the performance of the image classification. More specifically, the study used a multi-class classification by support vector machine (SVM) + convolutional neural network (CNN) for image classification analysis. SVM is a supervised learning model that analyzes data used for classification. CNN is a class of deep neural networks that is applied to analyzing visual imagery. The image classification comparison was made among four crops (paddy rice, potatoes, cabbages, and peanuts), roads, and structures for classification. In the first stage, the support vector machine handled the hyperspectral image classification through pixel-based analysis. Then, the convolution neural network improved the classification of image details through various blocks (cells) of segmentation in the second stage. A series of discussion and analyses of the results are presented. The repair module was also designed to link the usage of CNN and SVM to remove the classification errors. View Full-Text
Keywords: image classification; support vector machine; convolution neural network image classification; support vector machine; convolution neural network
Show Figures

Figure 1

MDPI and ACS Style

Wan, S.; Yeh, M.-L.; Ma, H.-L. An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data. ISPRS Int. J. Geo-Inf. 2021, 10, 242. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040242

AMA Style

Wan S, Yeh M-L, Ma H-L. An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data. ISPRS International Journal of Geo-Information. 2021; 10(4):242. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040242

Chicago/Turabian Style

Wan, Shiuan; Yeh, Mei-Ling; Ma, Hong-Lin. 2021. "An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data" ISPRS Int. J. Geo-Inf. 10, no. 4: 242. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040242

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop