@inproceedings{471037b63ed84f418f059578e95a7210,
title = "Remote Sensing Classification of Environmental Indicators",
abstract = "In Sweden 15 national environmental quality objectives have been defined together with specific environmental indicators to be analyzed repeatedly to follow relevant environmental trends. Remote sensing offers potential to assess wall-to-wall changes in the ecosystems. With the environmental indicators related to forestry as background the forest are divided into different classes. Remote sensing classification of this type of objects is complicated and reported classification rates are usually poor. To overcome most of the problems with traditional methods we use a new approach, where multispectral and change detection techniques are combined. Measures for quality assessment of the classified image are developed and applied. Furthermore, the importance of unbiased and high-precision estimates of the confusion matrix is emphasized. Even if the classified image gives incorrect proportions of the different classes unbiased area estimates can be derived from the confusion matrix",
keywords = "Remote sensing, classification, information theory, k-NN, probabilistic classifier, quality assessment, wavelet transform, Remote sensing, classification, information theory, k-NN, probabilistic classifier, quality assessment, wavelet transform",
author = "Jun Yu and Bo Ranneby",
year = "2003",
language = "English",
series = "Proceedings of ISRSE",
booktitle = "30th International Symposium on Remote Sensing of Environment - Information for Risk Management and Sustainable Development",
publisher = "ISRSE",
}