Abstract. Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Aiming at the mixed data composed of numerical and categorical attributes, a new unified dissimilarity metric is proposed, and based on that a new clustering algorithm is also proposed. The experiment result shows that this new method of clustering mixed data by fast search and find of density peaks is feasible and effective on the UCI datasets.

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The high-quality pansharpened image with both high spatial resolution and high spectral fidelity is highly desirable in various applications. However, existing pansharpening methods may lead to spatial distortion and spectral distortion. To measure the degrees of distortion caused by the pansharpening methods, we conduct in-deep studies on the subjective and objective quality assessment of pansharpened images. We built a subjective database consisting of 360 images generated from 20 couples of panchromatic (PAN)/multispectral (MS) images using 18 pansharpening methods. Based on the database, we proposed a no-reference quality assessment method to blindly predict the quality of pansharpened images via opinion-unaware learning. The proposed method first extracted features from the MS images' spectral bands and typical information indexes which comprehensively reflect spatial distortion, spectral distortion, and the effects of pansharpening on applications. Based on the features extracted from the pristine MS image training dataset, a benchmark multivariate Gaussian (MVG) model is learned. The distance between the benchmark MVG and the MVG fitted on the test image is calculated to measure the quality. The experimental results show the superiority of our method on our database.

In the process of economic urbanization, because of competition among cities, agglomerations and polarization of regional economies are produced. This paper studies the urban polarization with Chinese characteristics and the regional economic urbanization, which include the imbalance under the influence of different geographical factors between the east and west of China and the imbalance under the comprehensive influence of natural and human factors in the province. The urban economic polarization index (UEPI) is constructed to describe the regional imbalance caused by the economic polarization of capital cities in China. The purpose is to explore the polarization of provincial capitals in their respective provinces and to reveal the strength and evolution of their role in the imbalance of economic urbanization. Then, combined with relevant analysis of natural and socio-economic background data, the induced factors and the mechanism of urban polarization are diagnosed. The results show the following: (1) The UEPI can accurately measure the polarization level of provincial capitals through the calculation of typical cities. (2) Based on the UEPI, capital cities can be divided into four categories, which include inapparent, obvious, prominent, and striking. Different cities have different effects on the imbalance in economic urbanization. (3) The main inducing factors of urban polarization are the resource environment, policy system, industrial structure, investment, scientific and technological innovation, location, and extroversion. The policy system is often an important link that integrates and adjusts various factors to form a comprehensive driving mechanism.

The paper studies urban road traffic problems from the perspective of resource science. The resource composition of urban road traffic system is analysed, and the road network is proved as a scarce resource in the system resource combination. According to the role of scarce resources, the decisive role of road capacity in urban traffic is inferred. Then the new academic viewpoint of “wasteful transport” was proposed. Through in-depth research, the paper defines the definition of wasteful transport and expounds its connotation. Through the flow-density relationship analysis of urban road traffic survey data, it is found that there is a clear boundary between normal and wasteful transport in urban traffic flow. On the basis of constructing the flow-density relationship model of road traffic, combined with investigation and analysis, the quantitative estimation method of wasteful transport is established. An empirical study on the traffic conditions of the Guoding section of Shanghai shows that there is wasteful transport and confirms the correctness of the wasteful transport theory and method. The research of urban wasteful transport also reveals that: (1) urban road traffic is not always effective; (2) traffic flow exceeding road capacity is wasteful transport, and traffic demand beyond the capacity of road capacity is an unreasonable demand for customers; (3) the explanation that the traffic congestion should apply the comprehensive theory of traffic engineering and resource economics; and (4) the wasteful transport theory and method may be one of the methods that can be applied to alleviate traffic congestion.

The changes in the social development environment have put forward new requirements for talent training in higher vocational colleges. This paper analyzes the problems faced in the current talent training process, and proposes a talent training model of "integration of job, course, certificate and competition", which integrates the real project of the enterprise, the ability requirements of vocational qualification certification and vocational skills competition into the curriculum. It is of great significance to improve the quality of cloud computing talents training for computer network technology majors and to accelerate the construction of a modern vocational education system so that students have skills that match the needs of future occupations.

Different from natural image quality assessment methods, satellite stereo images have different requirements on quality in different application scenarios, which poses a huge challenge to establish a suitable objective evaluation model. In this paper, we focus on the quality evaluation of high resolution panchromatic (satellite stereo) images in specific application scenarios of building detection. First, we build a new satellite stereo image database (SSID), which consists of 400 distorted source satellite stereo images (SSIs) generated from the 20-source SSIs with two distortion types and 10-distortion strengths. We use detection accuracy scores to represent the quality of the SSIs, which is obtained through building detection, not subjective testing. We then propose an objective evaluation model based on joint dictionary learning. In the training phase, we bridge the features of the SSIs and the corresponding detection accuracy scores through joint dictionary learning. In the testing phase, we used sparse coding to get the quality of the testing image. The experimental results demonstrate the effectiveness of the proposed method.