A novel remotely sensed image classification based on ensemble learning and feature integration

To make full use of the multi-source remotely sensed data for classification, a novel method was proposed based on the integration of full-polarization SAR (HH, HV, VH, VV) data, features of polarization coherence matrix, spectral features provided by optical data, texture features extracted from optical and SAR data and multi-classifier ensemble. Preprocessing for full-polarization data was performed and polarimetric features are extracted from polarization coherence matrix. Spatial textural features including contrast, dissimilarity, second moment, etc., are extracted from PALSAR full-polarization data and optical image using Grey-level Co-occurrence Matrix (GLCM) method. Features of polarization coherency matrix, full-polarization SAR channels, spectral and textures are integrated by 6 strategies. Some well-known classification techniques, including Support Vector Machine (SVM), Minimum Distance (MD), Back Propagation Neural Network (BPNN), Multi-Layer Perceptron (MLP), Random Subspace (RSS), Random Forest (RF) classifiers were selected to test different combination strategies. The parallel and sequential ensemble learning techniques were selected to integrate single classifier for land cover classification. The results indicate that the proposed approach integrating multi-source, multi-features and multi-classifier strategy can make full use of the potential of optical and SAR remotely sensed data for landscape types, and improve the overall accuracy and the accuracy of single land cover type effectively.