This paper introduces a novel semi-supervised tri-training classification algorithm based on diversity measurement for hyperspectral imagery. In this algorithm, three measures of diversity, i.e., double-fault measure, disagreement metric and correlation coefficient, are applied to select the optimal classifier combination from different classifiers, e.g., support vector machine (SVM), multinomial logistic regression (MLR), extreme learning machine (ELM) and k-nearest neighbor (KNN). Then, unlabeled samples are selected using an active learning (AL) method, and consistent results of any other two classifiers combined with a spatial neighborhood information extraction strategy are employed to predict their labels. Moreover, a multi-scale homogeneity (MSH) method is utilized to refine the classification result with the highest accuracy in the classifier combination, generating the final classification result. Experiments on three real hyperspectral data indicate that the proposed approach can effectively improve classification performance.

This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, which are the main problems in the local discriminant embedding (LDE) and local Fisher discriminant analysis (LFDA) methods. An active learning method is then used to select the most useful and informative samples from the candidate set. In the experiments undertaken in this study, the three base classifiers were multinomial logistic regression (MLR), k-nearest neighbor (KNN), and random forest (RF). To confirm the effectiveness of the proposed RLDE method, experiments were conducted on two real hyperspectral datasets (Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS)), and the proposed RLDE tri-training algorithm was compared with its counterparts of tri-training alone, LDE, and LFDA. The experiments confirmed that the proposed approach can effectively improve the classification accuracy for hyperspectral imagery.

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To evaluate urban human settlement, we propose a human settlement environment development index (HSEDI) model by choosing vegetation coverage, land surface temperature, impervious surfaces, slope, wetness, and water condition as the evaluation factors. We applied the proposed model to Xuzhou City, Jiangsu Province, China. Landsat-5 Thematic Mapper (TM) images from 1998 to 2010 and digital elevation model (DEM) data with a 30-m resolution were used to calculate the values of the six evaluation factors. The HSEDI value in Xuzhou City was found to be between 2.24 and 8.10 from 1998 to 2010, and it was further divided into five levels, uninhabitable, moderately uninhabitable, generally inhabitable, moderately inhabitable, and inhabitable. The best HSEDI value was in 2007. The generally inhabitable region was about 100.98 km 2 , covering 30.87% of the total area in 2007; the moderately inhabitable region was about 170.58 km 2 covering 52.15% of the total area; the inhabitable region was about 32.03 km 2 , covering 9.79% of the total area; the percentage of the uninhabitable region was zero; and that of the moderately uninhabitable region was very small, less than 1.00%. Moreover, we analyzed the habitability in the respect of spatial patterns and change detection. Results show that the degraded regions of habitability quality are mainly located in the urban fringe and the improved regions are mainly located in the main urban and rural areas. Reason for the degraded habitability quality is the rapid progress of urbanization. However, the increase in urban green spaces and the construction of the main urban area promoted the improved habitability quality. Besides, we further analyzed socioeconomic and socio-demographic data to confirm the results of the habitability analysis. The results indicate that the human settlement in Xuzhou City is in a satisfactory condition, but some efforts should be made to control the possible uninhabitable and moderately uninhabitable regions, and to improve the quality of the generally inhabitable regions.