Foreword to the Special Issue on "GeoVision: Computer Vision for Geospatial Applications"

The nine papers in this special section focus on the development of new computer vision techniques for the interpretation of remote sensing images. These papers represent a follow-up of two workshops held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015, that was held in Boston, MA, EARTHVISION 2015 and MSF 2015. The purpose of both workshops and of this special issue is to foster fruitful collaboration of computer vision, Earth observation, and geospatial analysis communities.

[1]  Mihai Datcu,et al.  Immersive Interactive SAR Image Representation Using Non-negative Matrix Factorization , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[3]  Nicolas Courty,et al.  Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions , 2015, ArXiv.

[4]  Piotr Tokarczyk,et al.  Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Jaewook Jung,et al.  Results of the ISPRS benchmark on urban object detection and 3D building reconstruction , 2014 .

[6]  Gabriele Moser,et al.  Multimodal Classification of Remote Sensing Images: A Review and Future Directions , 2015, Proceedings of the IEEE.

[7]  Steffen Fritz,et al.  Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki , 2015 .

[8]  Konrad Schindler,et al.  An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Pietro Perona,et al.  Online crowdsourcing: Rating annotators and obtaining cost-effective labels , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[10]  Uwe Stilla,et al.  Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[11]  Konrad Schindler,et al.  Road networks as collections of minimum cost paths , 2015 .

[12]  Jie Chen,et al.  Multiclass Labeling of Very High-Resolution Remote Sensing Imagery by Enforcing Nonlocal Shared Constraints in Multilevel Conditional Random Fields Model , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Carlo Gatta,et al.  Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Shanshan Chen,et al.  Saliency Detector for SAR Images Based on Pattern Recurrence , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[16]  Huanxin Zou,et al.  Transfer Sparse Subspace Analysis for Unsupervised Cross-View Scene Model Adaptation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[18]  David A. Clausi,et al.  Fully Connected Continuous Conditional Random Field With Stochastic Cliques for Dark-Spot Detection In SAR Imagery , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Nikos Komodakis,et al.  Graph-Based Registration, Change Detection, and Classification in Very High Resolution Multitemporal Remote Sensing Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Aurélien Plyer,et al.  Adaptation and Evaluation of an Optical Flow Method Applied to Coregistration of Forest Remote Sensing Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Jamie Sherrah,et al.  Semantic Labeling of Aerial and Satellite Imagery , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Uwe Stilla,et al.  Combining Active and Semisupervised Learning of Remote Sensing Data Within a Renyi Entropy Regularization Framework , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.