Mapping of Agricultural Crops from Single High-Resolution Multispectral Images - Data-Driven Smoothing vs. Parcel-Based Smoothing
暂无分享,去创建一个
Konrad Schindler | Asli Ozdarici-Ok | Ali Özgün Ok | K. Schindler | A. O. Ok | Asli Ozdarici-Ok | A. Ozdarici-Ok | A. Ok
[1] Yan Gao,et al. Optimal region growing segmentation and its effect on classification accuracy , 2011 .
[2] Francisca López-Granados,et al. Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control , 2013, Precision Agriculture.
[3] Mustafa Turker,et al. Sequential masking classification of multi‐temporal Landsat7 ETM+ images for field‐based crop mapping in Karacabey, Turkey , 2005 .
[4] Lucy Marshall,et al. Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest , 2013 .
[5] Peter M. Atkinson,et al. A comparison of texture measures for the per-field classification of Mediterranean land cover , 2004 .
[6] H. Shimamura,et al. Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data , 2014 .
[7] Francesca Bovolo,et al. Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[8] Marie-Pierre Jolly,et al. Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.
[9] Mahesh Pal,et al. Support vector machine‐based feature selection for land cover classification: a case study with DAIS hyperspectral data , 2006 .
[10] T. M. Lillesand,et al. Remote Sensing and Image Interpretation , 1980 .
[11] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[12] P. Gong,et al. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China , 2011 .
[13] Liangpei Zhang,et al. A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[14] Graeme G. Wilkinson,et al. Results and implications of a study of fifteen years of satellite image classification experiments , 2005, IEEE Transactions on Geoscience and Remote Sensing.
[15] Nikos Komodakis,et al. Approximate Labeling via Graph Cuts Based on Linear Programming , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Ping Zhong,et al. Learning Conditional Random Fields for Classification of Hyperspectral Images , 2010, IEEE Transactions on Image Processing.
[17] Paul M. Mather,et al. Support vector machines for classification in remote sensing , 2005 .
[18] Mustafa Turker,et al. Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study , 2011 .
[19] Pedro Antonio Gutiérrez,et al. Object-Based Image Classification of Summer Crops with Machine Learning Methods , 2014, Remote. Sens..
[20] 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.
[21] Clement Atzberger,et al. Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas , 2012 .
[22] Mario Chica-Olmo,et al. An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .
[23] Michael T. Orchard,et al. Color quantization of images , 1991, IEEE Trans. Signal Process..
[24] Kellie J. Archer,et al. Empirical characterization of random forest variable importance measures , 2008, Comput. Stat. Data Anal..
[25] Shuhe Zhao,et al. Segmentation of multispectral high-resolution satellite imagery using log Gabor filters , 2010 .
[26] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[27] Jing Li Wang,et al. Color image segmentation: advances and prospects , 2001, Pattern Recognit..
[28] P. Gong,et al. Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .
[29] Zuhal Akyürek,et al. A segment-based approach to classify agricultural lands by using multi-temporal optical and microwave data , 2012 .
[30] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[31] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[32] Peter M. Atkinson,et al. Fine Spatial Resolution Simulated Satellite Sensor Imagery for Land Cover Mapping in the United Kingdom , 1999 .
[33] Francesca Bovolo,et al. A Context-Sensitive Clustering Technique Based on Graph-Cut Initialization and Expectation-Maximization Algorithm , 2008, IEEE Geoscience and Remote Sensing Letters.
[34] Paul M. Mather,et al. Classification methods for remotely sensed data, 2nd ed , 2016 .
[35] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[36] Richard Szeliski,et al. A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] P. Gong,et al. Accuracy Assessment Measures for Object-based Image Segmentation Goodness , 2010 .
[38] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[39] Chih-Jen Lin,et al. A Comparison of Methods for Multi-class Support Vector Machines , 2015 .
[40] Paul Aplin,et al. Sub-pixel land cover mapping for per-field classification , 2001 .
[41] P. Gong,et al. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama , 2004 .
[42] L. Janssen,et al. Integrating topographic data with remote sensing for land-cover classification. , 1990 .
[43] R. Pontius,et al. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment , 2011 .
[44] Mingjun Song,et al. A competitive pixel-object approach for land cover classification , 2005 .
[45] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[46] Liangpei Zhang,et al. Detail-Preserving Smoothing Classifier Based on Conditional Random Fields for High Spatial Resolution Remote Sensing Imagery , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[47] J. Six,et al. Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .
[48] P. Gong,et al. Frequency-based contextual classification and gray-level vector reduction for land-use identification , 1992 .
[49] Olga Veksler,et al. Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[50] Qihao Weng,et al. A survey of image classification methods and techniques for improving classification performance , 2007 .
[51] Giles M. Foody,et al. Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification , 2004 .
[52] Miguel Figueroa,et al. Competitive learning with floating-gate circuits , 2002, IEEE Trans. Neural Networks.
[53] Xiaojun Yang,et al. Parameterizing Support Vector Machines for Land Cover Classification , 2011 .
[54] Andrew Blake,et al. "GrabCut" , 2004, ACM Trans. Graph..
[55] Timothy A. Warner,et al. Segment based image classification , 2006 .
[56] Gunilla Borgefors,et al. Integrated method for boundary delineation of agricultural fields in multispectral satellite images , 2000, IEEE Trans. Geosci. Remote. Sens..
[57] Francisca López-Granados,et al. Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery , 2009 .
[58] Christopher Conrad,et al. Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data , 2010, Remote. Sens..
[59] Peng Gong,et al. A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data , 1992 .
[60] Shiming Xiang,et al. A Graph-Based Classification Method for Hyperspectral Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[61] Ute Beyer,et al. Remote Sensing And Image Interpretation , 2016 .
[62] P. Gong,et al. Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery , 2014 .
[63] Gabriele Moser,et al. Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[64] Anil K. Jain,et al. Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[65] Marie-Pierre Jolly,et al. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[66] Konrad Schindler,et al. An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[67] D. Lobell,et al. Climate and Management Contributions to Recent Trends in U.S. Agricultural Yields , 2003, Science.
[68] R. Fuller,et al. An integrated approach to land cover classification: An example in the Island of Jersey , 2001 .
[69] Peter M. Atkinson,et al. The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean , 2000 .