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.
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.