Machine Learning Techniques in Remote Sensing Data Analysis

Abstract

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. Introduction Challenges in remote sensingOwing 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 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 nearreal 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. General concepts of machine learningMachine 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 between different classes of patterns. In the field of remote sensing, descriptive machine learning algorithms often focus on land cover classifications, and thus provide important input information in several environmental monitoring systems, as for instance in the area of flood forecast, urban sprawl and land degradation. In this cont...

The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

“…Notably, the development of advanced machine learning tools further contributes to handling large multi-temporal remote sensing data [18]. This is because traditional classifiers, such as maximum likelihood, insufficiently manipulate complicated, high-dimensional remote sensing data.…”

Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

“…ANN is a computational model that is inspired by the human brain. ANN is formed by a collection of interconnected units (neurons) that learn from experience by modifying connections (weights) [56,57]. ANN usually consists of an input layer, hidden layer, and output layer.…”

Foreword to the Special Issue on Information Extraction From High-Spatial-Resolution Optical Remotely Sensed Imagery

“…Despite the abundant information in addition to the spectral signatures brought by high spatial resolution, traditional spectral-based and pixel-based methods usually fail to accomplish these tasks because of the large spectral diversity of same objects, complex spatial relationships between objects, and the huge data volume. Many efforts have been made by the remote sensing community to address the problems including the fusion of multiple data sources [6], object-based image processing [7], [8], various textural and semantic features [9], the use of machine learning algorithms [10], scene understanding, etc. Meanwhile, new techniques are developed for the emerging demands as environmental assessment and population estimation.…”