#Gaussian software ncsu code#

Keywords: RBF neural network Forest resources Socio-economic factors Nonlinear fitting Intelligent managementĬontact information: School of Economic Management, Inner Mongolia Agricultural University, Hohhot 010018, P.R. The results provide a new way and show an application potential for the analysis and intelligent management in forest resources.Ĭomparative Analysis of Dynamic Changes in Forest Resources with RBF Neural Network and Regression Method At the same time, the RBF neural network can analyze and evaluate the importance of influencing factors simply and conveniently. The results showed that the RBF neural network method can be applied in modeling the dynamic changes of forest resources and showed a higher prediction accuracy over the traditional statistical modeling approaches. In this paper, the radial basis function (RBF) neural network was introduced to study the relationship between the changes of forest resources and socio-economic factors and was evaluated by comparison with the traditional multiple-linear regression model. Also, its explicit mathematical model is inconvenient for intelligent management. There are difficulties in accurately analyzing it by using traditional multivariate-statistical methods.

However, the relationship between forest resources and social economic factors is normally a multivariate nonlinear relationship. ' Comparative analysis of dynamic changes in forest resources with RBF neural network and regression method,' BioResources 17(2), 2313-2330.įorest resources are the most important natural resources their dynamic changes (growth or decline) are affected by socio-economic factors, and to study their linkage is of great significance.

Thank all team members for contributions on project idea design, software implementation and experiment documentation.Wu, X., Bao, Q., and Liu, G.

Thank all TAs for suggestions on both algorithms and software tools to faciliate project development.

Raju Vatsavai for the support and advice on this project.

Shijie Li (email: Tianpei Xia (email: Jingjuan Deng (email: Zhiren Lu (email: Zifan Nan (email: Rui Liu (email: Thank Prof.

#Gaussian software ncsu code#

The existence of various versions at the same time is for the sake of result comparisons and code optimization. We use both git and hand-labeled file names for versioning.

Orfeo ToolBox (OTB) - Used to view and process satellite imagesĪll team members contribute equivalently to this development of this project.

Chrome - The web brower used to view and edit ipython notebook.

For example, temporal changes are detected and marked as red and yellow blocks in the following pair of mapsįor more details and intermediate results, please check the ipython notebooks in the folder code Built With The final product of our code execution should be a pair of maps on the same landscape area with major changes marked in transparent color blocks. Python high_resolution_image_change_detection.py Prerequisitesįollowing Python packages have to be installed before executing the project code All experiments are executed on local machine. The models and algorithms of our project is implemented in Python code with the help of IPython Notebook for data and result visualization.

Then a Gaussian Mixture Model (GMM) is used to cluster the KL divergence map into various levels of change, which is a good visualization of change detection between the two images over time. Next, we use symmetric Kullback-Leibler (KL) divergence of two Gaussian distributions at the same location of various time as metrics of image changes.

In our project, we divide each high-resolution satellite image into equally-sized square grids and fit each grid of RGB pixels using one Gaussian distribution. By given two satellite images from the same area, it can identify changes accurately. This grid based method is helpful in Bi-temporal change detection. We explore a detection method to identify landscape changes using high resolution satellite images. High resolution images provided by satellites are good resource to use to identify and quantify landscapes changes. Project: GMM based Change Detection Framework Using Very High-resolution Satellite Imagery