1 Machine learning techniques in remote sensing data analysis

Several applications have been developed in the field of remote sensing image analysis during the last decades. Besides well-known statistical approaches, many recent methods are based on techniques taken from the field of machine learning. A major aim of machine learning algorithms in remote sensing is supervised classification, which is perhaps the most widely used image classification approach. In this chapter a brief introduction to machine learning and the different paradigms in remote sensing is given. Moreover this chapter briefly discusses the use of recent developments in supervised classification techniques such as neural networks, support vector machines and multiple classifier systems. Owing to the recent development of different Earth observation platforms with increased spatial and spectral resolution as well as higher revisit times, remote sensing provides more detailed information on land cover and the environmental state than ever before. Moreover, different Earth-observation systems, such as multi-spectral and SAR systems operate in different wavelengths, ranging from visible to microwave. The data sets consequently provide different, but complementary information. The classification of such data might be considered Kernel Methods for Remote Sensing Data Analysis Edited by Gustavo Camps-Valls and Lorenzo Bruzzone 4 INTRODUCTION complex on the one hand, but with regard to recent and upcoming missions, remote sensing applications become even more attractive, on the other. Many early techniques have been taken directly from signal processing and these methods are often based on simple data models and approaches. However, when dealing with recent data sets these well-known classifiers can be limited (Richards 2005). In addition, increased performance requirements such as speed (e.g., for operational monitoring systems and near-real time applications) and accuracy, further demand the development of more sophisticated analysis concepts (Jain et al. 2000). Thus, the development of adequate methods for various data sets is an important ongoing research topic in the field of remote sensing. This chapter is organized as follows. In Section 1.1.2 a general introduction to machine learning is given, followed by a discussion on different paradigms in remote sensing. In Section 1.2 various supervised classifiers are introduced. A conclusion is given in Section 1.3. Machine Learning is an area of artificial intelligence and generally refers to the development of methods that optimize their performance iteratively by learning from the data. Such methods can be predictive (e.g., a regression model) and make a prediction of a specific phenomenon or descriptive (e.g., a classification model) and distinguish for example …