Ideal solutions are an important part of the technique for order preference by similarity to ideal solution (TOPSIS) based on vague sets. In order to expand and supplement the ideal solutions of TOPSIS based on vague sets, and considering the potential influence on the degree of truth-membership and the degree of false-membership by the degree of unknown in vague sets, three potential ideal solutions are proposed by looking for new ideal solution forms between the maximal ideal solutions and the actual ideal solutions. Proof in theoretical shows that all the proposed potential ideal solutions are vague sets, which means that they all can apply to the calculation of TOPSIS based on vague sets. The proposed potential ideal solutions are three types of supplementary forms for the ideal solutions of TOPSIS based on vague sets. Several properties of the proposed potential ideal solutions are discussed, and it shows that the proposed potential ideal solutions can be converted to the maximal ideal solutions and the actual ideal solutions under certain conditions. The proposed potential ideal solutions are applied to landmark preference based on TOPSIS, which effectiveness and feasibility are illustrated, and a new way for landmark preference is provided in the meanwhile.
Multi-feature classification and image segmentation are two cores in object-oriented classification method of high resolution remote sensing images. Multi-feature object identification is an important part of multi-feature classification, which is identification for the image regions or the segmentation objects segmented by image segmentation under the guidance of a corresponding relationship between objects and features or combination. A method of multi-feature object identification was proposed based on vague soft sets. Firstly, the vague soft sets were formed by building the parameter sets according to spectral characteristics and object-oriented features of the segmentation objects. Secondly, according to general TOPSIS (the Technique for Order Preference by Similarity to Ideal Solution), a TOPSIS based on Vague soft sets for multi-feature object identification was proposed, which obtained a object identification result of the segmentation objects by using similarity measure of vague soft sets to sort attribution of the cover types for the segmentation objects. The experimental results show that the proposed method obtains a correct result of object identification and is feasible and effective.
To make full use of the data resources and realize a sharing for the different types of data in different industries, a method of format conversion between CAD data and GIS data based on ArcGIS was proposed. To keep the integrity of the converted data, some key steps to process CAD data before conversion were made in AutoCAD. For examples, deleting unnecessary elements such as title, border and legend avoided the appearance of unnecessary elements after conversion, as layering data again by a national standard avoided the different types of elements to appear in a same layer after conversion. In ArcGIS, converting CAD data to GIS data was executed by the correspondence of graphic element classification between AutoCAD and ArcGIS. In addition, an empty geographic database and feature set was required to create in ArcGIS for storing the text data of CAD data. The experimental results show that the proposed method avoids a large amount of editing work in data conversion and maintains the integrity of spatial data and attribute data between before and after conversion.