Area extraction of winter wheat at county scale based on modified multivariate texture and GF-1 satellite images

Winter wheat is grown in a wider area in China. Monitoring its planting area is therefore a key link for national food security. And area extraction is based on the availability of data source. As the first new satellite of GF series domestic satellites，GF-1 satellite realizes the combination of high spatial resolution, multi-spectrum, and wide field of view (WFV) which has been applied in agricultural monitoring nearly three years. It is necessary to evaluated GF-1 imagery for agricultural applications, especially for the planting area monitoring of food crops. In this paper, we studied the effectiveness of area extraction of winter wheat at a county scale using the WFV imagery from GF-1 satellite. An approach using both spectral information and multivariate texture was proposed in order to make full use of spatial structure information in satellite imagery to further improve classification accuracy of the stable crop. Firstly, the multivariate texture was extracted through modeling based on the modification of multivariate variogram, and the model parameter measured spatial correlation with respect to all the bands of a multispectral image was designed as a distance metric computed by mapping the Mahalanobis distance between spatial point pair into the Euclidean space so that the Mahalanobis distance can be computed as Euclidean distance. We realized this transformation through the Cholesky decomposition of the covariance matrix item in the Mahalanobis distance expression. Then the derived multivariate texture image was combined with spectral data, and the fusion of spectral and texture information was input into the supervised classification technique of support vector machine to identify winter wheat. Two GF-1 images acquired on November 2013 and January 2014 with four spectral bands (blue, green, red, and near-infrared) and 16 m pixel size covering the large area of winter wheat in Suixi county in Anhui province were selected as remotely sensed data source. Classifications were generated based on the proposed method using the modified multivariate texture information and other two traditional methods using spectral data alone and plus traditional texture images at bi-temporal, respectively. Accuracy assessments based on test samples were used to evaluate classification results. Compared with those from other two traditional methods, classification results from the proposal method had a significant improvement with the overall accuracy of 4.12% and 2.36% in seeding stage, and with the overall accuracy of 2.59% and 0.94% in overwintering stage. Z-test statistics were computed for paired comparisons among three classification results at bi-temporal respectively, and the confidence level values were all less than 2.5%. Comparative analysis was carried out by computing the relative errors between the extracted area from classification results of the proposed method and the statistical area for winter wheat in some certain quadrats. With no consideration of the possible objective influence from imaging quality, phenology or measurement precision of quadrats, accuracies of the extracted area from classification results of the proposed method in quadrats were generally better than 90%. Then a Rapideye image covering the same area acquired on April 2014 with a higher resolution was used as a reference to evaluate the availability of the proposal method in the larger test regions with size of 5 km × 5 km. Consistency of the extracted area of winter wheat from classifications using GF-1 images and the reference data from Rapideye image in test regions were able to achieve nearly 97% when winter wheat grown stably. From the results of experiments, it was found that fusion of multivariate texture and spectral information with GF-1 satellite images could significantly improve the overall accuracy of winter wheat identification, and the proposed approach presented in this study also can be useful for different crops in other regions.