Hyperspectral image classification for mapping agricultural tillage practices

Qiong Ran; Wei Li; Qian Du; Chenghai Yang

doi:10.1117/1.JRS.9.097298

6 March 2015 Hyperspectral image classification for mapping agricultural tillage practices

Qiong Ran, Wei Li, Qian Du, Chenghai Yang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 097298 (March 2015). https://doi.org/10.1117/1.JRS.9.097298

Abstract

An efficient classification framework for mapping agricultural tillage practice using hyperspectral remote sensing imagery is proposed, which has the potential to be implemented practically to provide rapid, accurate, and objective surveying data for precision agricultural management and appraisal from large-scale remote sensing images. It includes a local region filter [i.e., Gaussian low-pass filter (GLF)] to extract spatial-spectral features, a dimensionality reduction process [i.e., local fisher’s discriminate analysis (LFDA)], and the traditional k-nearest neighbor (KNN) classifier, and is denoted as GLF-LFDA-KNN. Compared to our previously used local average filter and adaptive weighted filter, the GLF also considers spatial features in a small neighborhood, but it emphasizes the central pixel itself and is data-independent; therefore, it can achieve the balance between classification accuracy and computational complexity. The KNN classifier has a lower computational complexity compared to the traditional support vector machine (SVM). After classification separability is enhanced by the GLF and LFDA, the less powerful KNN can outperform SVM and the overall computational cost remains lower. The proposed framework can also outperform the SVM with composite kernel (SVM-CK) that uses spatial-spectral features.

Citation Download Citation

Qiong Ran, Wei Li, Qian Du, and Chenghai Yang "Hyperspectral image classification for mapping agricultural tillage practices," Journal of Applied Remote Sensing 9(1), 097298 (6 March 2015). https://doi.org/10.1117/1.JRS.9.097298

Published: 6 March 2015

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