代理國外軟體

12月銷售軟體排行

1. MindManager 視覺化思考繪圖軟體
2. EViews 預測分析計量軟體
3. LISREL 線性結構分析軟體

4.

ATLAS.ti 定性量化分析軟體
5.

EndNote 參考書目軟體

6.

Stata 資料管理統計繪圖軟體

7. See5/C5.0  資料探勘軟體
8. HLM 階層分析軟體
9.

Expert Choice  AHP專家決策分析軟體

10. Grapher 3D科學繪圖軟體

 

 

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IDRISI Selva    地理圖像處理軟體

IDRISI Selva是整合地理信息系統和圖像處理軟件解決方案,提供近300個數字空間信息的分析和顯示模塊。IDRISI提供最廣泛的地理信息系統和圖像處理工具,在行業中一個單一的,負擔得起的包。與IDRISI,所有的分析功能來標準有沒有必要購買昂貴的附加軟件來擴展你的研究能力。

IDRISI Selva is an integrated GIS and Image Processing software solution providing nearly 300 modules for the analysis and display of digital spatial information. IDRISI offers the most extensive set of GIS and Image Processing tools in the industry in a single, affordable package. With IDRISI, all analytical features come standard—there is no need to buy costly add-ons to extend your research capabilities.

IDRISI Selva provides:
A complete GIS analysis package for basic and advanced spatial analysis, including tools for surface and statistical analysis, decision support, and change and time series analysis
A complete Image Processing system with the most extensive hard and soft
   classifers in the industry, including machine learning classifiers such as neural
   networks and classification tree analysis, as well as image segmentation for
   classification
Integrated modeling environments including the Earth Trends Modeler for
    image time series of environmental trends and Land Change Modeler for land
    change analysis and prediction, a critical component for REDD projects
Complete utilities for import and export along with a comprehensive set of
    documentation and tutorials.

 

With IDRISI Selva, you can:
Analyze trends and anomalies of image time series with the innovative Earth Trends Modeler facility.

The Earth Trends Modeler is a vertical application included with IDRISI Selva and specially designed for the analysis of image time series. It includes a coordinated suite of data mining tools for the extraction of trends and the underlying determinants of variability.

 

The SEGMENTATION module creates an image of segments that have spectral similarity across many input bands. The image on the left uses a larger similarity threshold than the one on the right, resulting in more generalized, less homogeneous segments. Using this threshold, the image allows for segments that wholly contain building objects.

 

 

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