The objective of image analysis is to generate information from remotely sensed data. Your task is to identify a framework for image processing.

Image Processing
[ pdf ]

Source: Trodd N, 2007. Designing Digital Image Analysis: Image Processing.

This section includes Jensen et al., unknown. Module 1: The remote sensing process, in Remote Sensing Core Curriculum, Volume 3, Introductory Digital Image Processing. <http://www.cas.sc.edu/geog/rslab/Rscc/fmod1.html> URL last accessed 07-09-2007.

Model for Remote Sensing Data Analysis
[ pdf ]
Source: Madhok V & Landgrebe DA, 2002. A process model for remote sensing data analysis, IEEE Transactions on Geoscience and Remote Sensing, 40, 680-686.
Synergy in Remote Sensing
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Source: Cracknell AP, 1998. Synergy in remote sensing - what's in a pixel? International Journal of Remote Sensing, 19, 2025-47.


Best practice in image analysis is to build and invert a model for image interpretation and then run the inverted model on image data. Your task is to better understand the concepts of parameterising a model, forward modelling and model inversion.

Modelling and Model Inversion
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Source: Trodd N, 2007. Designing Digital Image Analysis: Modelling and Model Inversion.

Information Extraction Principles
[ URL ]

Source: Landgrebe D, 1998. Information extraction principles & methods for multispectral & hyperspectral image data, Chapter 1 in Chen C.H. (ed.) Information Processing for Remote Sensing, River Edge, New Jersey : World Scientific Publishing. <http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf> URL last accessed 16-10-2011.

Model Inversion to Estimate Vegetation Water Content
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Source: Zarco-Tejada PJ et al., 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods, Remote Sensing of Environment, 85, 109–124.
Inverting a Canopy Reflectance Model
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Source: Kuusk A, 1998. Monitoring of vegetation parameters on large areas by the inversion of a canopy reflectance model, International Journal of Remote Sensing, 19, 2893-2905.

Read the chapter on "Remote sensing: from data to understanding" written by Paul Curran and colleagues in 1998. It provides a well-structured overview of the current model-based approach to digital image analysis. This is an important exercise and you should prepare detailed notes that explain the following - What is the relationship between phenomena of interest, state variable(s) and remotely sensed data? What domains of information are available for Earth observation by remote sensing? What is meant by the dimensionality of Earth observation data?

Information Domains
[ pdf ]

Source: Trodd N, 2007. Designing Digital Image Analysis: Information Domains.

Remote Sensing: From Data to Understanding

Source: Curran PJ et al., 1998. Remote sensing: from data to understanding. Ch 3 in Longley et al. (eds.) Geocomputation: a primer. Wiley : Chichester. p33-59.

Multi-angle Remote Sensing
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Source: Diner DJ et al., 1999. New directions in Earth observing: scientific application of multi-angle remote sensing, Bulletin of the American Meteorological Society, 80, 2209-2228.
Selecting Remotely Sensed Data Dimensions
[ pdf ]
Source: Phinn SR, 1998. A framework for selecting appropriate remotely sensed data dimensions for environmental monitoring & management, International Journal of Remote Sensing, 19, 3457-3463.
e-Tutorial: SPLAT  
Task Your task is to read the articles by Prenzel (2004) and Coomber et al. (2004) that develop models to monitor change in land cover and then complete the e-Tutorial to design a method to monitor change in the Yangtze delta, PR China.
Land-cover and Land-use Change
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Source: Prenzel B, 2004. Remote sensing-based quantification of land-cover and land-use change for planning, Progress in Planning, 61, 281-299.
Automated Land Cover Monitoring
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Source: Coomber AJ, et al., 2004. Application of knowledge for automated land cover change monitoring, International Journal of Remote Sensing, 25 (16), 3177-3192.
e-Tutorial: Design a Method