Research On Machine Learning Methods For Hyperspectral Imagery Classification

Hyperspectral imagery classification is one of the key technologies in application of hyperspectral remote sensing, and is of great significance for resource investigation, environment monitoring, fine agriculture, surveying and mapping, and battlefield detection. To fulfill the requirement of hyperspectral imagery classification for accuracy, speed and reliability, considering the characteristic of correlation in high dimension and non-linear separability, imagery classification and dimensionality reduction were studied in depth using machine learning methods. The main works and creations of this dissertation are listed as follows:1. To solve the problem of slow speed of hyperspectral imagery non-negative matrix factorization (NMF) based on multiplicative update rule, a fast factorization algorithm based on improved projection grads was given. Using the block coordinate descent method, the global optimization of NMF was transformed into two sub-optimization problems solved by iteration, and each sub-optimization problem computed by improved projection grads. Through experiments it can be seen that this method increases the convergence speed of NMF evidently.2. A radial basis function (RBF) kernel parameters selection method was given to solve the problem of kernel parameter selection in hyperspectral imagery generalized discriminant analysis (GDA) feature extraction. Firstly, the number range of training samples was standardized and parameter space was dispersed logarithmically, then parameter was obtained by cross-validation. The analysis of experiments shows that, using this approach to select kernel parameter, GDA feature extraction can improve the accuracy of hyperspectral classification evidently.3. A reduced set (RS) based on support vector machine (SVM) hyperspectral imagery classification method was brought forward. Using the sequence minimum optimized algorithm and cross validation grid searching parameter selection method, a high precision multi-class SVM classifier is constructed. Reduced set vectors are obtained by solving the pre-image problem through differential evolution algorithm. It is validated that SVM has good generalization ability and RS-SVM can keep classification precision and increase the speed of classification.4. A relevance vector machine (RVM) based on hyperspectral imagery fuzzy classification method was brought forward. In this algorithm, sequence sparse Bayesian learning algorithm was used to improve the training speed of RVM. For the multi-class RVM classifier constructed by one against one decomposition method, we transform the probability of pairwise coupling classifier into membership of the ground objects classes. Compared with SVM through experiments, RVM parameter selection is simpler, and the training and classifying speed is faster. Using fuzzy membership can label mixed pixels and improve the reliability of imagery classification effectively.5. An AdaBoost based hyperspectral imagery ensemble classification method was put forward. Firstly decision stump was selected as weak classifier, and then this classifier can be boosted into subsection linear strong classifier by gentle AdaBoost algorithm, finally multi-class classification was designed by one against rest decomposition method. The speed of training and classifying was proved fast, and the classification accuracy of this method is better than many other methods.6. Both theoretically and experimentally, classification accuracy, training and test speed of SVM, RVM and AdaBoost were analyzed, and their different potentials in different hyperspectral imagery classification applications are pointed out. SVM is suitable for fine classification of ground objects which does not require real-time processing. RVM is applicable to classification of ground objects with statistical prediction. AdaBoost can be applied in fast classification which requires high precision.