Soft Classification and Assessment of Kalman Filter Neural Network for Complex Landcover of Tropical Rainforests

This work implemented a soft classification of neural network using Kalman filter algorithm (KFNN) for complex land cover mapping. Back propagation neural network (BPNN), SVM and maximum likelihood (MLC) were applied as comparisons. Using `hard´ and `fuzzy´ confusion matrices, the classifications were assessed. The KFNN outperformed other classifiers in terms of overall accuracy and Kappa statistics. Shannon Entropy and Confusion Index (CI) were estimated, and we found the uncertainty of classified pixels over the study area is relatively low as their membership values are not distributed evenly.