GEOG8711 Advanced Digital Image Analysis GE

2016

4.5

2 x 50-minute lectures weekly
2 x 50-minute computer labs weekly

1 of GEOG3018, GEOG3711, GEOG8003 has been successfully completed

It is assumed students will have skills and knowledge in Image Analysis such as can be found in GEOG8702 Image Analysis in Remote Sensing GE or GEOG8004 Image Analysis in Remote Sensing

This topic will allow students to specialise and develop skills in advanced remote sensing technologies. The topic will primarily consider the basic principles and prospective applications for Imaging Spectrometry (Hyperspectral Remote Sensing) for Earth observation, while the nature and application of RADAR and LIDAR remote sensing will also be demonstrated. The topic will focus on the reflective properties of materials; airbourne and satellite-based hyperspectral sensors; spectral mixing problematics; geometric and radiometric calibration; endmember selection; spectral unmixing and material mapping. Students will use digital image analysis software specifically designed to manage and analyse high spectral dimensional data

This topic allows students to specialise and develop skills in advanced remote sensing technologies, inlcuding Imaging Spectrometry, RADAR and LIDAR remote sensing. While there is a focus on each of these technologies, the primary aim of the topic is to provide students with a good grounding in the fundamental principles and prospective applications of Imaging Spectrometry (Hyperspectral Remote Sensing) for Earth resourse mapping and monitoring. The topic will furnish students with skills and knowledge of vocational value applicable to a wide range of areas such as Biodiversity Conservation, Earth Sciences, Geography, and Environmental Monitoring.

At the completion of the topic, students are expected to be able to:

1. Explain and discuss the principal concepts of RADAR and LIDAR remote sensing and their use in a range of applications
2. Optimise hyperspectral image content and visualisation through a range of geometric, radiometric and image transformation techniques
3. Extract useful information from hyperspectral data through a range of dedicated image processing techniques
4. Undertake a sound approach to sub-pixel material mapping
5. Undertake a sound approach to classification accuracy assessment
6. Be competent users of ERDAS IMAGINE software and its modules dedicated to the analysis of RADAR, LIDAR and Hyperspectral Imagery
7. Be able to successfully communicate the results of image analysis in remote sensing
8. Produce written work in accordance with good scholarly practice