Foreword to the Special Issue on Pattern Recognition in Remote Sensing

T HE constant increase in the amount of remotely sensed images as well as the urgent need for the extraction of useful information from such data sets have made the development of new pattern recognition techniques a popular research topic for several decades. This data volume, together with the complexity of the content acquired by a diverse set of sensors, 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. Therefore, a wide range of pattern recognition techniques have been proposed for both traditional application areas such as land cover and land use classification, road network extraction, and agricultural mapping and monitoring, as well as more recent topics such as monitoring of human settlements, management of natural resources, response planning for natural and human-induced disasters, assessment of the impact of climate change, and conservation of biodiversity. One of the important challenges in the combined fields of pattern recognition and remote sensing is the increasing resolution of the data that has led to an expansion in the data volume and an increase in the complexity of the analysis algorithms. Higher resolution often applies to the spatial characteristics of the images where 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, as well as synthetic aperture radar (SAR) data, so that proper techniques that are capable of modeling the peculiar statistical properties of each type of data are used. Finally, performance evaluation of the developed supervised, semi-supervised, unsupervised, batch and active learning algorithms is also an interesting problem given the limited availability of detailed ground truth data sets. This special issue is associated with the 6th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS 2010) [1] that was held in Istanbul, Turkey, on August 22, 2010 in conjunction with the IAPR International Conference on Pattern Recognition (ICPR 2010) with co-sponsorship by IAPR and IEEE Geoscience and Remote Sensing Society. The PRRSWorkshop, that 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