Projects:2018s1-108 Machine Learning Multi-Spectral Simulation - Revision history

A1684511 at 02:56, 22 October 2018

2018-10-22T02:56:10Z

2018-10-22T02:55:49Z

2018-10-22T02:17:35Z

2018-10-14T02:00:45Z

‎Introduction

2018-08-23T05:21:17Z

2018-08-23T05:20:55Z

2018-08-23T05:13:50Z

Created page with " == Introduction =="

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== Introduction ==

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 # From the directional subimages, form texture feature vector that corresponds to every pixel.
 # Perform fuzzy C-means clustering analysis on all texture feature vectors. Suppose g clusters are created, the cluster centres are denoted by '''CC<sub>1</sub>,CC<sub>2</sub>,...,CC<sub>g</sub>'''.
 == Project Members ==

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 === Merging Process ===
-# Create RAG and NNG and calculate the weights of edges base on similarity
+Create RAG and NNG and calculate the weights of edges base on similarity
 distance as described below.
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 feature distance.
-<math>d_{spe} = \sum_{i=1}^{n}\left(\text{std}_i - \frac{a \times \text{std}_{ai} + b \times \text{std}_{bi}}{a+b}\right)</math>
+[[File:spec_eq.png]]
 == Project Members ==

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 == Introduction ==
 This project aims to develop a machine learning system for generating simulated real-world environments from satellite imagery. The environments generated will be incorporated into VIRSuite, a multi-spectral real-time scene generator. The system will need to generate material properties to allow VIRSuite to render the environment realistically from visible to long-wave infrared spectra. The system will need to be able to regenerate existing environments as new satellite images become available, but doesn't need to be real-time or closed loop. Secondary phases of the project are to build a validation process to check that generated environments match real-world likeness across all spectra. The satellite imagery available is up to 0.3m per pixel and the environments generated should reflect this. This project requires students to: gain knowledge in deep and convolutional machine learning technologies, develop and adapt machine learning techniques for satellite imagery (GIS Data), and conduct validation experiments using multi-spectral knowledge and real-life data.
 == Project Members ==
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 * [[Alwin Kai En Ng]]
 * [[Thomas Focareta]]

← Older revision		Revision as of 02:00, 14 October 2018
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	== Introduction ==		== Introduction ==
		+	This project aims to develop a machine learning system for generating simulated real-world environments from satellite imagery. The environments generated will be incorporated into VIRSuite, a multi-spectral real-time scene generator. The system will need to generate material properties to allow VIRSuite to render the environment realistically from visible to long-wave infrared spectra. The system will need to be able to regenerate existing environments as new satellite images become available, but doesn't need to be real-time or closed loop. Secondary phases of the project are to build a validation process to check that generated environments match real-world likeness across all spectra. The satellite imagery available is up to 0.3m per pixel and the environments generated should reflect this. This project requires students to: gain knowledge in deep and convolutional machine learning technologies, develop and adapt machine learning techniques for satellite imagery (GIS Data), and conduct validation experiments using multi-spectral knowledge and real-life data.

	== Project Members ==		== Project Members ==

@@ Line 5: / Line 5: @@
 === Supervisors ===
-[[Danny Gibbins]]
+* [[Danny Gibbins]]
-[Sebastien Wong]]
+* [[Sebastien Wong]]
 === Students ===
-[[Alwin Kai En Ng]]
+* [[Alwin Kai En Ng]]
-[[Thomas Focareta]]
+* [[Thomas Focareta]]