Pattern Recognition in Remote Sensing

Development of new pattern recognition techniques for the analysis of data collected from satellites and airborne sensors used for Earth observation has been a popular research topic for several decades. Consequently, the pattern recognition and remote sensing research fields have always overlapped. However, the large volumes of remote sensing data acquired from last generation sensors require new advanced algorithms and techniques for automatic analysis. This data volume, together with new applications ranging from monitoring of human settlements to management of natural resources, from agricultural studies to response for natural and human-induced disasters, from the assessment of the impact of climate change to conserving biodiversity, require new interdisciplinary work involving the application of novel pattern recognition techniques to unsolved problems in remote sensing image analysis that cannot be handled by using traditional remote sensing methods. One of the most important challenges is the increasing resolution of the data, leading to an expansion in the data volume and an increase in the complexity of the analysis algorithms. Higher resolution often means that additional patterns are visible in large scenes and, therefore, more elaborate yet faster techniques need to be developed to detect and recognize them. Furthermore, a characteristic peculiar to remote sensing is that ‘‘high” resolution may mean not only ‘‘high spatial”, but also ‘‘high spectral” resolution, leading to a wealth of problems related to high dimensionality of feature spaces, and ‘‘high temporal” resolution, requiring new methods for time series analysis. Researchers also need to take into account the nature of different sensors used for collecting data with different modalities such as multi-spectral and hyper-spectral data, synthetic aperture radar (SAR) data, and light detection and ranging (LIDAR) data for developing proper techniques capable to model the peculiar statistical properties of each type of data in the implemented methodologies. Finally, performance evaluation of the developed supervised, semi-supervised, and unsupervised algorithms is also an interesting problem given the limited availability of detailed ground truth data sets. This special issue is associated with the 5th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2008) that was held in Tampa, FL, USA on December 7, 2008 in conjunction with the IAPR International Conference on Pattern Recognition (ICPR 2008) with co-sponsorship by IAPR and IEEE Geoscience and Remote Sensing Society. The PRRS Workshop which is implemented by the IAPR Technical Committee 7 on Remote Sensing offers an opportunity for researchers to gain a better understanding of the many diverse research topics in remote sensing that require contributions from the pattern recognition community, and has established itself as an important event for scientists involved in the combined fields of pattern recognition and remote sensing.