Distribution Characteristics and Optimization Strategies of Spatial Attractiveness of the Roof Garden in Meilecheng Commercial Complex of Handan City

With the rapid development of urban commercial complex in China,its roof garden becomes an important container for outdoor consumption and activities as the link between itself and the city,and more and more attention has been paid to its attractiveness.First,roof garden of Handan Meilecheng is taken as research object,and quantitative method of spatial attractiveness is established based on the number of spontaneous and chain activities and their intensity score.On this basis,data are collected via field investigation,and spatial attractiveness of roof garden of Meilecheng is quantified,and its distribution characteristics are summarized.Second,the cause of the characteristics formation is explained from the perspective of built environment elements.Finally,corresponding optimization strategies are proposed from four aspects:setting of landscape platform,increase of interest in sculpture sketches,design of space boundary,and improvement of spatial accessibility,which could provide reference for planning,design and transformation of roof garden of commercial complex in the future.

Integration of spatial attractions between and within pixels for sub-pixel mapping

As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and be- tween them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub- pixel/pixel spatial attraction model （SPSAM）, are described by the modified SPSAM （MSPSAM） that estimates the attractions accord- ing to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model （MSAM） for sub-pixel mapping is proposed that integrates the spatial attractions both within pix- els and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is em- ployed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM （MPS）, MSAM can provide higher accuracy and more rational sub-pixel mapping results.

A variation pixels identification method based on kernel spatial attraction model and local entropy for robust endmember extraction

A variation pixels identification method was proposed aiming at depressing the effect of variation pixels, which dilates the theoretical hyperspectral data simplex and misguides volume evaluation of the simplex. With integration of both spatial and spectral information, this method quantitatively defines a variation index for every pixel. The variation index is proportional to pixels local entropy but inversely proportional to pixels kernel spatial attraction. The number of pixels removed was modulated by an artificial threshold factor α. Two real hyperspectral data sets were employed to examine the endmember extraction results. The reconstruction errors of preprocessing data as opposed to the result of original data were compared. The experimental results show that the number of distinct endmembers extracted has increased and the reconstruction error is greatly reduced. 100% is an optional value for the threshold factor α when dealing with no prior knowledge hyperspectral data.