The advent of modern remote sensors alongside the development of advanced parallel
computing has significantly transformed both the theoretical and real implementation …

Foreword to the Special Issue on Pattern Recognition in Remote Sensing Page 1 IEEE
JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE …

Forest detection in remote sensing data is essential for important applications such as
detection of area desertification, flooding simulation, forest health analysis, or conversion of …

Machine learning offers the potential for effective and efficient classification of remotely
sensed imagery. The strengths of machine learning include the capacity to handle data of …

Supervised classifiers are the most popular approach for image classification due to their
high accuracies, ease of use and strong theoretical grounding. Their primary disadvantage …

Different remote sensing techniques act as an alternative to traditional fieldwork; thereby,
providing an efficient and time-saving approach to extract land use land cover (LULC) …

This study evaluated the synergistic use of Landsat5 TM and SPOT5 images for improving
forest classification using an object-based image analysis approach. Three image …

When original aerial photographs are combined with deep learning to classify forest
vegetation cover, these photographs are often hindered by the interlaced composition of …

Although the processing of multi-spectral imagery from earth observation satellites has been
effectively used for classification of many types of land cover, forest classification has …

Machine learning algorithms have shown a surprisingly successful development within the
last years. Several data intensive technical and scientific fields–like search engines, speech …