Remote sensing monitoring of wheat aphids by combining HJ satellite images with least squares twin support vector machine model - HU Gensheng; WU Wentian; LUO Juhua; HUANG Wenjiang; LIANG Dong; HUANG Linsheng (浙江大学学报. 农业与生命科学版)
Pests and diseases have become serious because of global warming, which have caused great economic losses to agricultural production, and have threatened human life and health, so it was very urgent and challenging to prevent or control pests and diseases. Real-time dynamic monitoring of the occurrence of pests and diseases in large scale continuous space can guide the prevention or control work accurately and effectively to reduce the impact of pests and diseases as well as the environmental pollution caused by the indiscriminate use of pesticides. Remote sensing technology can provide effective information for crop pests and diseases monitoring quickly and accurately on a massive continuous spatial surface. HJ- 1A/1B satellite has a high revisit period (4 days) . Multi- spectral images obtained by HJ- 1A/1B satellite sensors have high spatial resolution (30 m) and are very suitable for the monitoring of agricultural pests and diseases.The occurrence of wheat aphids affects seriously the yield and quality of wheat. Monitoring of the wheat aphids accurately and timely is helpful for effective prevention and control of pests. In this paper, by using the field location survey data and the HJ-CCD and HJ-IRS image data, the growth factors and the environmental factors of wheat are extracted, including normalized difference vegetation index (NDVI) , green normalized difference vegetation index (GNDVI) , reflectance of red band, land surface temperature (LST) and perpendicular drought index (PDI) . These factors had a great influence on the occurrence of wheat aphids. The monitoring model of wheat aphids in Tongzhou District and Shunyi District of Beijing was established by using the least squares twin support vector machine (LSTSVM) . The LSTSVM has a good processing ability for large scale unbalanced data and has stronger robustness than the traditional support vector machine (SVM) . Computational complexity of LSTSVM is reduced by using the least squares algorithm to transform inequality constraints into equality constraints.Experimental results showed that: the overall monitoring accuracy of the LSTSVM model was 86.4% and the Kappa coefficient was 0.71; the traditional SVM model was 77.3% and 0.52; the Fisher linear discriminant analysis (FLDA) model was 77.3% and 0.54; and the learning vector quantization (LVQ) neural network model was 72.7% and 0.39. In sum, the algorithm proposed in this paper has higher precision than the traditional SVM, FLDA and LVQ neural network.