ENVI

ENVI is a commercial remote sensing and image processing software package. It is developed by ITTVIS, and is currently at version 4.7. It provides an environment for displaying and processing remotely sensed data, and includes a large number of processing tools (such as image filtering, topographic modelling and image classification), all of which are accessible through a graphical user interface.

ENVI is written in IDL, and has an extensive IDL application programming interface, thus allowing ENVI's processing tools to be called from IDL programs. This then allows processing to take place with no user input, making these batch processing tasks suitable for use on High Performance Computing systems such as Iridis.

ENVI can be extended by writing IDL code, and a number of extensions (some of which have been developed by members of the University of Southampton) have been released for free at the ITTVIS Code Library.

At the University of Southampton, ENVI is available on all staff UDE machines (it can be installed from Start->All Programs->Additional Software), and a number of student workstation clusters.

For queries about this topic, contact Robin Wilson.

Projects

Automated selection of suitable atmospheric calibration sites for satellite imagery

Robin Wilson (Investigator)

Ground calibration targets (GCTs) play a vital role in atmospheric correction of satellite sensor data in the optical region, but selecting suitable targets is a subjective and time- consuming task. This project is developing methods to automatically select suitable GCTs, using a combination of remotely sensed multispectral and topographic data.

The application of automated pattern metrics to surface moisture influences on modelled dune field development

Robin Wilson, Joanna Nield (Investigators)

Areas of sand dunes (known as dunefields) develop complex patterns over time. These are influenced by both the past and present environmental conditions, including surface moisture, vegetation distribution and human impact. This project develops a method of automated pattern analysis which allow the patterns produced by a large number of sand dune evolution simulations (performed using the DECAL model) to be quantified over time.

People

Reno Choi
Senior Research Fellow, Geography (FSHS)

Robin Wilson
Postgraduate Research Student, Geography (FSHS)

Petrina Butler
Administrative Staff, Research and Innovation Services