Web-Based Supervised Thematic Mapping

We introduce a methodology for semiautomatic thematic map generation from remotely sensed Earth Observation raster image data based on user-selected examples. The methodology is based on a probabilistic k-nearest neighbor supervised classification algorithm. Efficient operation is attained by exploiting data structures for high-dimensional indexing. The methodology is integrated in a Web-mapping server that is coupled to an HTML supervision interface that supports interactive navigation as well as model training and tuning. Quantitative classification quality and performance measurements are extracted for real optical data with 0.25 m resolution on a highly diverse training area.

[1]  Jorma Laaksonen,et al.  Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  J. Six,et al.  Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .

[4]  Xiaopeng Zhang,et al.  Robust Rooftop Extraction From Visible Band Images Using Higher Order CRF , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Anil M. Cheriyadat,et al.  Unsupervised Feature Learning for Aerial Scene Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Lei Guo,et al.  Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Antonio J. Plaza,et al.  A Web-Based System for Classification of Remote Sensing Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Martin Brandt,et al.  Woody vegetation and land cover changes in the Sahel of Mali (1967-2011) , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[9]  David M. Mount,et al.  On the Efficiency of Nearest Neighbor Searching with Data Clustered in Lower Dimensions , 2001, International Conference on Computational Science.

[10]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[11]  Hugo Carrão,et al.  Combining per-pixel and object-based classifications for mapping land cover over large areas , 2014 .

[12]  Marco Quartulli,et al.  A review of EO image information mining , 2012, 1203.0747.

[13]  Marco Quartulli,et al.  Visual analytics for built-up area understanding from metric resolution Earth observation data , 2013 .

[14]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[15]  Mihai Datcu,et al.  Interactive learning and probabilistic retrieval in remote sensing image archives , 2000, IEEE Trans. Geosci. Remote. Sens..

[16]  Basilio Sierra,et al.  Trace Transform Based Method for Color Image Domain Identification , 2014, IEEE Transactions on Multimedia.

[17]  Gui-Song Xia,et al.  Learning High-level Features for Satellite Image Classification With Limited Labeled Samples , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Matthias Blume,et al.  Image annotation based on learning vector quantization and localized Haar wavelet transform features , 1997, Defense, Security, and Sensing.

[19]  Reynold Cheng,et al.  Evaluating probability threshold k-nearest-neighbor queries over uncertain data , 2009, EDBT '09.

[20]  Bertrand Le Saux,et al.  Boosting for interactive man-made structure classification , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[21]  Paul E. Keel.  Collaborative Visual Analytics: Inferring from the Spatial Organization and Collaborative Use of Information , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[22]  Ho Van Quan,et al.  A web-enabled visualization toolkit for geovisual analytics , 2012, Inf. Vis..

[23]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[24]  Ujjwal Maulik,et al.  Efficient parallel algorithm for pixel classification in remote sensing imagery , 2012, GeoInformatica.

[25]  Emmanuel Christophe,et al.  The Orfeo Toolbox remote sensing image processing software , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[26]  Xavier Perrotton,et al.  Automatic Object Detection on Aerial Images Using Local Descriptors and Image Synthesis , 2008, ICVS.

[27]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[28]  Basilio Sierra,et al.  Architecture for semi-automatic multimedia analysis by hypothesis reinforcement , 2009, 2009 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting.

[29]  Mihai Datcu,et al.  Building Virtual Earth Observatories Using Ontologies, Linked Geospatial Data and Knowledge Discovery Algorithms , 2012, OTM Conferences.

[30]  Shawn D. Newsam,et al.  Geographic Image Retrieval Using Local Invariant Features , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Marco Pastori,et al.  Information mining in remote sensing image archives: system concepts , 2003, IEEE Trans. Geosci. Remote. Sens..