Remote sensing image classification using layer-by-layer feature associative conditional random field

For the difficulty of expressing spatial context in classification of high resolution remote sensing imagery, a new multi-scale Conditional Random Field( CRF) model was proposed here. Specifically, a given image was represented as three superpixel layers respectively being region, object and scene from fine to coarse firstly. Then features were extracted layer-bylayer, and those features from the three layers were associated with each other to form a feature vector for each node in region layer. Secondly, Support Vector Machine( SVM) was adopted to define association potential function, and Potts model weighted by feature contrast function was used to define interaction potential function of CRF model, thus a layer-by-layer feature associative and multi-scale SVM-CRF model was formed. To confirm the effectiveness of the proposed model in classification, experiments on two complex scenes from Quickbird remote sensing imagery were developed. The results show that the proposed model achieves an improved accuracy averagely 2. 68%, 2. 37%, 3. 75% higher than that of SVM-CRF model based on either region, object or scene layer, also it consumes less time in classification.