Study on Ensemble Crop Information Extraction of Remote Sensing Images Based on SVM and BPNN

High resolution remote sensing image contains abundant information, but remote sensing classification only based on spectral information is affected in the complex spectrum area. Crop area and other land-cover objects contain different texture features. This paper extracts texture information based on gray-level co-occurrence matrix and Gabor filters group, sets up spectrum-texture joint feature set. To enhance classification efficiency, Ensemble learning strategy is introduced to improve classical support vector machine and back propagation neural network classifiers in training process. To prove the effectiveness of proposed methods, several experiment images are utilized to execute experiments. Results indicate that proposed methods improve classification accuracy compared with classical algorithms significantly, and promote running efficiency compared with the situation of large sample, support corn area statistical process and yield estimation.