Crop identification using superpixels and supervised classification of multispectral CBERS-4 wide-field imagery

Maciel Zortea; Eduardo Rocha Rodrigues

doi:10.1117/12.2532843

21 October 2019 Crop identification using superpixels and supervised classification of multispectral CBERS-4 wide-field imagery

Maciel Zortea, Eduardo Rocha Rodrigues

Author Affiliations +

Proceedings Volume 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI; 111491U (2019) https://doi.org/10.1117/12.2532843
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Remote sensing has been increasingly used in monitoring and analyzing agricultural activities. Crop identification based on analysis of individual pixel response, without considering neighboring pixels, may lead to poor results. In this paper, we investigate the use of superpixels generated by the Simple Linear Iterative Clustering (SLIC) algorithm to delineate homogeneous regions in images to help crop identification. The proposed classification strategy consists of combining pixel-level classification probabilities, estimated using the pixel spectral response, with pooled probability values of the pixels located inside superpixels. Weighting both probability contributions produce a hybrid classification. We test this idea to map the interim-harvest of corn and cotton in an agricultural area in Mato Grosso State, Brazil, characterized by the presence of large farms. For this, we use a cloudfree multispectral image captured by the wide-field imaging camera onboard the China-Brazil Earth Resources Satellite 4 (CBERS-4), that acquires four bands in the visible and near-infrared with a pixel spatial resolution of 64 m/pixel. The two main crops in our study area where identified with an overall accuracy of about 85%. Encouraging results suggest that the proposed method may be used as part of a remote sensing-based crop identification system.

Citation Download Citation

Maciel Zortea and Eduardo Rocha Rodrigues "Crop identification using superpixels and supervised classification of multispectral CBERS-4 wide-field imagery", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491U (21 October 2019); https://doi.org/10.1117/12.2532843

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available