Object-based classification of hyperspectral data using Random Forest algorithm

Abstract This paper presents a new framework for object-based classification of high-resolution hyperspectral data. This multi-step framework is based on multi-resolution segmentation (MRS) and Random Forest classifier (RFC) algorithms. The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images. Given the high number of input features, an automatic method is needed for estimation of this parameter. Moreover, we used the Variable Importance (VI), one of the outputs of the RFC, to determine the importance of each image band. Then, based on this parameter and other required parameters, the image is segmented into some homogenous regions. Finally, the RFC is carried out based on the characteristics of segments for converting them into meaningful objects. The proposed method, as well as, the conventional pixel-based RFC and Support Vector Machine (SVM) method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics. These data were acquired by the HyMap, the Airborne Prism Experiment (APEX), and the Compact Airborne Spectrographic Imager (CASI) hyperspectral sensors. The experimental results show that the proposed method is more consistent for land cover mapping in various areas. The overall classification accuracy (OA), obtained by the proposed method was 95.48, 86.57, and 84.29% for the HyMap, the APEX, and the CASI data-sets, respectively. Moreover, this method showed better efficiency in comparison to the spectral-based classifications because the OAs of the proposed method was 5.67 and 3.75% higher than the conventional RFC and SVM classifiers, respectively.

[1]  George P. Petropoulos,et al.  Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using Support Vector Machines , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Norman Kerle,et al.  Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data , 2012 .

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  G. Hay,et al.  Object-Based Image Analysis , 2008 .

[5]  Clement Atzberger,et al.  Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data , 2012, Remote. Sens..

[6]  Reza Safari,et al.  Modified algorithm based on support vector machines for classification of hyperspectral images in a similarity space , 2012 .

[7]  Ravi Shankar Dwivedi,et al.  Comparison of classifiers of remote-sensing data for land-use/land-cover mapping , 2004 .

[8]  André Stumpf,et al.  bject-oriented mapping of urban trees using Random Forest lassifiers , 2013 .

[9]  Austin Troy,et al.  Object-based high-resolution land-cover mapping , 2009, 2009 17th International Conference on Geoinformatics.

[10]  Yanchen Bo,et al.  Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data , 2015, Remote. Sens..

[11]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[12]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[13]  M. Neubert,et al.  A COMPARISON OF SEGMENTATION PROGRAMS FOR HIGH RESOLUTION REMOTE SENSING DATA , 2004 .

[14]  Onisimo Mutanga,et al.  Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers , 2014 .

[15]  Kuangnan Xiong,et al.  Roughened Random Forests for Binary Classification , 2014 .

[16]  Wolfgang Reinhardt,et al.  Image segmentation for the purpose of object-based classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[17]  Bing Zhang,et al.  A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information , 2014 .

[18]  Beniamino Murgante,et al.  A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation , 2016, ISPRS Int. J. Geo Inf..

[19]  Joydeep Ghosh,et al.  Investigation of the random forest framework for classification of hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[20]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[21]  O. Csillik,et al.  Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[22]  Geoffrey J. Hay,et al.  Object-based Image Analysis : Strengths , Weaknesses , Opportunities and Threats ( Swot ) , 2006 .

[23]  Eléonore Wolff,et al.  Segmentation of very high spatial resolution satellite images in urban areas for segment-based classification , 2005 .

[24]  Johannes R. Sveinsson,et al.  Random Forest Classification of Remote Sensing Data , 2006 .

[25]  G. Hay,et al.  An automated object-based approach for the multiscale image segmentation of forest scenes , 2005 .

[26]  Naif Alajlan,et al.  Fusion of supervised and unsupervised learning for improved classification of hyperspectral images , 2012, Inf. Sci..

[27]  R. Q. Feitosa,et al.  MULTIRESOLUTION SEGMENTATION : A PARALLEL APPROACH FOR HIGH RESOLUTION IMAGE SEGMENTATION IN MULTICORE ARCHITECTURES , 2010 .

[28]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[29]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[30]  K. Itten,et al.  Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX) , 2015 .

[31]  Thomas Blaschke,et al.  Object-Based Image Analysis , 2008 .

[32]  Gexiang Zhang,et al.  A Hybrid Classifier Based on Rough Set Theory and Support Vector Machines , 2005, FSKD.

[33]  Jon Atli Benediktsson,et al.  Advances in Spectral-Spatial Classification of Hyperspectral Images , 2013, Proceedings of the IEEE.

[34]  Lorenzo Bruzzone,et al.  A Composite Semisupervised SVM for Classification of Hyperspectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[35]  Tang Ping,et al.  Comparison study on the pixel-based and object-oriented methods of land-use/cover classification with TM data , 2008, 2008 International Workshop on Earth Observation and Remote Sensing Applications.

[36]  Hao Jiang,et al.  A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates , 2012, Sensors.

[37]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[38]  Jon Atli Benediktsson,et al.  Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images , 2013, Proceedings of the IEEE.

[39]  K. Kyrimis.  Monitoring land cover change detection with remote sensing methods in Magnesia prefecture in Greece. , 2000 .

[40]  Peter W. Eklund,et al.  A study of parameter values for a Mahalanobis Distance fuzzy classifier , 2003, Fuzzy Sets Syst..