A multilevel decision fusion approach for urban mapping using very high-resolution multi/hyperspectral imagery

A novel multilevel decision fusion approach is proposed for urban mapping using very-high-resolution (VHR) multi/hyperspectral imagery. The proposed framework consists of three levels: (1) at level I, we first propose a self-dual filter for extracting structural features from the VHR imagery–subsequently, the spectral and structural features are integrated based on a weighted probability fusion; (2) level II extends level I by implementing the spectral–structural fusion in an object-based framework; and (3) at level III, the object-based probabilistic outputs at level II are used to identify unreliable objects, and shape attributes of these unreliable objects are then considered for refinement of classification. At this level, a decision-level object merging is used to improve the initial segmentation, since shape feature extraction is highly dependent on the quality of segmentation. Experiments were conducted on a Hyperspectral Digital Imagery Collection Experiment (HYDICE) DC Mall image and a QuickBird Beijing data set. The results revealed that the proposed approach provided progressively increasing accuracies when the multilevel features were gradually considered in the processing chain.

[1]  Jon Atli Benediktsson,et al.  Classification of remote sensing images from urban areas using a fuzzy possibilistic model , 2006, IEEE Geoscience and Remote Sensing Letters.

[2]  Y. Ouma,et al.  On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation , 2006 .

[3]  Austin Troy,et al.  Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study , 2009 .

[4]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Liangpei Zhang,et al.  Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.

[6]  N. Lam,et al.  Wavelets for Urban Spatial Feature Discrimination: Comparisons with Fractal, Spatial Autocorrelation, and Spatial Co-Occurrence Approaches , 2004 .

[7]  Mihai Datcu,et al.  Coarse-to-Fine Approach for Urban Area Interpretation Using TerraSAR-X Data , 2010, IEEE Geoscience and Remote Sensing Letters.

[8]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[9]  Surya S. Durbha,et al.  Feature Identification via a Combined ICA–Wavelet Method for Image Information Mining , 2010, IEEE Geoscience and Remote Sensing Letters.

[10]  Liangpei Zhang,et al.  An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[11]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[12]  Jon Atli Benediktsson,et al.  Classification and feature extraction for remote sensing images from urban areas based on morphological transformations , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  Frieke Van Coillie,et al.  Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium , 2007 .

[14]  Jonathan Cheung-Wai Chan,et al.  Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[15]  S. Franklin,et al.  OBJECT-BASED ANALYSIS OF IKONOS-2 IMAGERY FOR EXTRACTION OF FOREST INVENTORY PARAMETERS , 2006 .

[16]  Jon Atli Benediktsson,et al.  Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas , 2009, EURASIP J. Adv. Signal Process..

[17]  Pierre Soille,et al.  Advances in mathematical morphology applied to geoscience and remote sensing , 2002, IEEE Trans. Geosci. Remote. Sens..

[18]  Anne Puissant,et al.  The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery , 2005 .

[19]  Liangpei Zhang,et al.  An Adaptive Multiscale Information Fusion Approach for Feature Extraction and Classification of IKONOS Multispectral Imagery Over Urban Areas , 2007, IEEE Geoscience and Remote Sensing Letters.

[20]  Xin Huang,et al.  A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform , 2008 .

[21]  Y. Ouma,et al.  Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery , 2008 .

[22]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[23]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[24]  Lorenzo Bruzzone,et al.  A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[25]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[26]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[27]  Peijun Li,et al.  Multispectral image segmentation by a multichannel watershed‐based approach , 2007 .

[28]  Zuyuan Wang,et al.  Color- and Texture-Based Image Segmentation for Improved Forest Delineation , 2007, IEEE Transactions on Geoscience and Remote Sensing.