Research On Hyperspectral Imagery Classification Based On Parallel Support Vector Machine In Cloud Environment

Hyperspectral remote sensing images have the characteristics of many wavebands, huge data volume, data uncertainty and the Hughes phenomenon effect in supervised classification, so the existing technology of image analysis and information processing must be put forward higher requirements. Support vector machine (SVM) is an effective learning scheme based on statistical learning theory and has been confirmed by numerous experiments. SVM has been successfully applied to hyperspectral classification because it can solve the problems of small samples, nonlinearity and high dimension. But the traditional SVM algorithm (serial) is inefficient in training and predicting for large-scale hyperspectral images. The one-machine and the traditional distributed environment can hardly provide outstanding parallel computing abilities and sufficient memory space for mass-data processing. Therefore, in this paper, the technologies of parallel support vector machine (PSVM) and cloud computing are introduced, the classification model of parallel support vector machine based on cloud computing (Cloud-PSVM) is devised, as well as the incremental learning algorithm and kernel parameters global optimization strategy of Cloud-PSVM are proposed under the cloud computing environment. The Cloud-PSVM is applied to land uses classification, and the classification cloud services of hyperspectral images are built based on Hadoop platform. The computation mode, classification methods, and service model are taken into consideration throughout the research, so as to improve the efficiency of hyperspectral images classification under the premise of guaranteeing classification accuracy, and promote the large-scale and smart extraction and machine interpretation of ground objects information from hyperspectral images. The main work and contributions are summarized as follows:(1) In order to improve the spatial resolution of Hyperion hyperspectral images effectively, an improved Gram-Schmidt fusion method for hyperspectral images is devised to fuse Hyperion hyperspectral images and AL1high spatial resolution images efficiently from the same platform with the same time phase. A combined radial basis kernel function (MRBF) based on the integrated features of spectrum-terrain and texture is proposed, and the binary decision tree multi-class SMO (BDT-SMO) classifier based on MRBF is built, which can improve the accuracy of hyperspectral fusion images classification effectively. (2) A Hadoop cloud storage platform is developed that allows distributed storage of large-scale hyperspectral fused images and sample data through the use of the Hadoop distributed file system (HDFS) and the Hbase database. Large-scale fused images and sampled data can be accessed more efficiently by selecting proper segmentation strategies, access schemes and forms of data organization.(3) In order to improve parallel learning efficiency of large-scale training sample effectively, a improved hybrid parallel support vector machine (YBJCF-PSVM) model based on cross-samples is proposed, and it can be combined with GPU to improve parallel learning ability of single node. In addition, a Cloud-PSVM classifier is devised based on Map Reduce and YBJCF-PSVM model.(4) Cloud-PSVM is used in the classification of land uses. The MapReduce model is adopted to extract features of hyperspectral fused images from experimental zones in a parallel manner, and large-scale samples can be trained and predicted parallelly by Cloud-PSVM classifier. Experimental results show that Cloud-PSVM classifier can considerably improve the classification efficiency of hyperspectral fused images under the premise of guaranteeing classification accuracy. In addition, in order to improve the release efficiency of the results of land use classification effectively, the cloud services of hyperspectral fusion images classification are also devised and implemented based on Hadoop.(5) A incremental learning algorithm of SVM is devised based on MapReduce and hull vectors (MapReduce-HASVM) under cloud computing environment which can improve the generalization and scalability of the Cloud-PSVM classifier effectively. In addition, a distributed global parameters optimization strategy of Cloud-PSVM based on cloud computing and parallel genetic algorithm (PGA) is proposed, and it can effectively improve the classification accuracy and kernel parameters optimization efficiency of Cloud-PSVM classifier.