## Summary

As described in Chapter 1, the European Union needs to be able to estimate the area of an agricultural field in order to manage its agricultural subsidy system. A cost-effective way to estimate the area of many fields on a frequent basis is to use an automated system that determines the area of each field from satellite remote sensing images. In general, such a system works by allocating each pixel to the class it resembles most, followed by counting the number of pixels per class over the...

## A little comparative experiment based on MAIS data

From the previous section it has become clear that the concept of ILS is simple and easily extendible to an arbitrary number of classes. Furthermore, we have shown that the method has the potential to be fast as its complexity depends on the number of classes instead of the number of spectral bands. However, it must still be checked that the number of iterations required is not too large for ILS to be applicable in practice. Besides that, the accuracy of the fractions vector estimated by ILS...

## Brute force approximation

To understand the complex relations between the ei, a look at the n-dimensional feature space is helpful. According to convex geometry principles, pixels that are composed of c Figure 3.3 Maximum likelihood solution of the physical linear mixture model. To solve the model, one point pi per endmember has to be determined, whose Mahalanobis distance to mi is minimal. The sum-to-one constraint demands that these points lie on a line through x the positivity constraint requires that pi and p2 lie...

## Results of edge detection

The performance of the Frei-Chen edge detector is dependent on the setting of its threshold. Figure 5.8 shows the specificity, sensitivity, and average percentage correct achieved for a wide range of threshold settings5. As expected, at low thresholds many pixels are marked as edge and therefore mixed pixels, which leads to a high sensitivity but a low specificity. If the threshold is raised, the sensitivity decreases while the specificity increases, ultimately reaching 100 . The best...

## Neural networks

In the last decade neural networks have emerged as a powerful classification tool for, amongst others, remotely sensed image data (Benediktsson et al. 11 , Hepner et al. 47 , Kanellopoulos et al. 57 ). An artificial neural network (ANN) is a system of highly connected but simple processing units called neurons, which is inspired by the architecture of the human brain. Calibration of an ANN is done by repeatedly presenting a set of training examples to it, which has the advantage that the class...

## Introduction to remote sensing

The field of remote sensing is very broad and has been described from many angles by numerous authors, e.g. Campbell 19 , Lillesand and Kiefer 66 , and Sabins 88 . In his book, Campbell tried to identify the central concepts of remote sensing and came up with the following definition Remote sensing is the practice of deriving information about the earth's land and water surfaces using images acquired from an overhead perspective, using electromagnetic radiation in one or more regions of the...

## Analytic approximation

As was done for the statistical model, the optimal solution of the physical model can be approximated using a closed formula as well. Once again it is required that all variance-covariance matrices Ni are identical, which can be accomplished by taking the mean matrix N 1 c C 1 Ni. As long as the Ni are small relative to the dispersion of the endmembers if not, the estimated solution is probably poor anyway the resulting loss of accuracy is not very large Horwitz et al. 51 . Under the assumption...