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In this paper, we propose a model based on the bag of mixed-gram terms to deal with sentiment classification task and extracting sentimental features. We obtain a very short-dimensional vector to represent sentiment and use the sentimental representations to complete the task of sentiment classification. Furthermore, since the sentimental representations and some traditional word vectors have complementary advantages, we combine the sentimental representations with convolutional neural networks that use other word vectors and ultimately implement a more efficient classifier. Experimental results show that this combination method can use static word vectors to deal with sentimental classification tasks well, and the sentimental representations here play the role of fine-turned word vectors in previous research.

Since a large proportion of pixels are often composed of mixed land cover types within remote sensing images, how to eliminate the impact of the error resulting from pixel mixing effect in the estimation of land surface emissivity on the accuracy of land surface temperature (LST) retrieval from remote sensing data is a key problem to resolve firstly in process of LST retrieval. Based on the local relationship between the thermal radiance of one pixel and that of its components satisfying the Planck's radiance function, in this paper, the mono-window algorithm was improved by integrating with mixed-pixel classification and sub-pixel mapping for Landsat imagery. Validation indicates that the improved mono-window algorithm is able to provide more accurate LST than the original algorithm for Landsat TM/ETM+ imagery. By applying the improved algorithm to the Landsat image of Shanghai, the result revealed the spatial heterogeneity characteristics of UHI effect in Shanghai city.

Research into pixel unmixing in remote sensing imagery led to the development of soft classification methods. In this article, we propose a possibilistic c repulsive medoids (PCRMdd) clustering algorithm which attempts to find c repulsive medoids as a minimal solution of a particular objective function. The PCRMdd algorithm is applied to predict the proportion of each land use class within a single pixel, and generate a set of endmember fraction images. The clustering results obtained on multi-temporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) images of Shanghai city in China reveal the spatio-temporal pattern of Shanghai land use evolvement and urban land spatial sprawl in course of urbanization from 1989 to 2002. The spatial pattern of land use transformation with urban renewal and expansion indicates the urban land use structure is gradually optimized during vigorous urban renewal and large-scale development of Pudong area, which will have an active influence on improving urban space landscape and enhancing the quality of the ecological environment. In addition, accuracy analysis demonstrates that PCRMdd represents a robust and effective tool for mixed-pixel classification on remote sensing imagery to obtain reliable soft classification results and endmember spectral information in a noisy environment.

This paper introduces a novel algorithm for delineating mutually exclusive service areas of shelters which have finite capabilities to accommodate residents who are unevenly distributed in space. Minimizing the travel cost and keeping spatial continuity with capability constraint are the convergent objectives of this algorithm. The algorithm partitions an entire residential area into a number of spatial units to handle the unevenness of demand, and employs a shift insertion mechanism to reduce travel cost and to improve the spatial continuity of service areas. A series of experiments and scenarios has been conducted for the city of Memphis, USA, to validate the proposed algorithm. Comparative experiments under different constraints show that the proposed algorithm is a feasible solution for delineating service areas. Various application circumstances (for example, travel-cost calculation methods and the granularity of available demographic data) are discussed to evaluate the algorithm's feasibility. The proposed algorithm can be applied not only to delineating service areas of existing shelters but also to optimizing location allocation of new shelters or other facilities with similar characteristics.

Investigating the evolvement process of Mesoscale Convective Systems (MCSs) over the Tibetan Plateau using satellite remote sensing image sequence is a very important and effective method of forecasting heavy rainfall. This paper presents a spatial data mining approach, by which a possible heavy rainfall forecast can be made, based on MCS tracking using remote sensing satellite images. Firstly, an automatic method for object tracking from the satellite image sequence is proposed, aiming at identification of the qualified MCSs, their characteristics and their moving trajectories. Then, a novel two-phase spatial data mining framework is designed to enable the deduction of the correlations and causalities between MCS activities and possible heavy rainfall occurrences. The proposed approach proves to be capable of lifting the heavy burden of manual rainfall forecasting from the shoulders of meteorologists, by automatically analyzing and interpreting massive, meteorological remote sensing data sets to assist weather forecasting.

This paper presents a new system of indicators for ecological assets assessment and a method for evaluating ecological total assets. With Remote Sensing images of Pudong New Area in 1998 and 2005, we got down to calculating vegetation coverage, Vegetation Net Primary Productivity (NPP) and adjustment factor. The distribution pattern of ecological assets of Pudong New Area in 1998 and 2005 as well as the ecological environment degradation between them were analyzed and mapped using DEM, meteorological and mentioned ancillary data. The whole area was finally divided into five: fast-growing area, moderate growth area, basically maintained area, moderately degraded area and severely degraded area, of which the basically maintained areas account for a large proportion, however, there are a majority of degraded areas, and the growing areas don’t reach to 10%. Which reveal the ecological environment in Pudong New Area actually and tell us to spare no efforts to making friends with our nature.

Identification and tracking of moving targets from image sequence is an active branch of image processing and computer vision field. In meteorology, automatic tracking of the trajectories of convective systems from meteorological satellite remote sensing images remains a challenging issue. To monitor the evolution of Mesoscale Convective Systems (MCS) over the Tibetan Plateau in China in summer, an automatic tracking approach based on point-pattern matching is proposed using GMS brightness temperature data. The method tracks the trajectories of MCS by determining the spatial location relationship between sets of points representing MCSs on consecutive images. The experimental results demonstrate the capability of the proposed method to track the evolution of MCS life cycles, especially which have smaller area and faster velocity of displacement. Furthermore, the tracking results show the activity frequency and geographic distribution, and the trajectories and propagation of MCS over the Tibetan Plateau from June to August 1999-2000.

Investigating the characteristics of mesoscale convective clouds based on satellite infrared images is very important for strong precipitation forecast. This paper proposes a new method for cloud identification, tracking and characterization using time-varying satellite infrared cloud image sequences. After clouds are identified using image processing techniques, a couple of features are extracted as their representations, based on which feature correspondences of different clouds in consecutive images are computed to track the same cloud. Then, clouds are finally characterized according to their convective activities. This automatic cloud-tracking alternative is applied to investigate the characteristics and moving tracks of the mesoscale convective clouds that move out of the Tibetan Plateau. Experimental results are also provided to demonstrate its capability to perform correct tracking in different situations.