Automatic Detection of Geospatial Objects Using Multiple Hierarchical Segmentations

The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback-Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes.

[1]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[2]  B. S. Manjunath,et al.  Modeling and Detection of Geospatial Objects Using Texture Motifs , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  J. Tilton,et al.  Analysis of hierarchically related image segmentations , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[4]  Alexei A. Efros,et al.  Using Multiple Segmentations to Discover Objects and their Extent in Image Collections , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Sebastiano B. Serpico,et al.  A Markov random field approach to spatio-temporal contextual image classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[6]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

[7]  Selim Aksoy,et al.  Learning bayesian classifiers for scene classification with a visual grammar , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[8]  A.J. Plaza,et al.  Automated selection of results in hierarchical segmentations of remotely sensed hyperspectral images , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[9]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[10]  Greg Hamerly,et al.  Learning the k in k-means , 2003, NIPS.

[11]  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.

[12]  Ah-Hwee Tan,et al.  On Quantitative Evaluation of Clustering Systems , 2003, Clustering and Information Retrieval.

[13]  Leen-Kiat Soh,et al.  ARKTOS: an intelligent system for SAR sea ice image classification , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[14]  S. Aksoy,et al.  Multi-resolution segmentation and shape analysis for remote sensing image classification , 2005, Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005. RAST 2005..

[15]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[16]  Selim Aksoy,et al.  Morphological Segmentation of Urban Structures , 2007 .

[17]  Chi-Ren Shyu,et al.  Automated object extraction through simplification of the differential morphological profile for high-resolution satellite imagery , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[18]  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).

[19]  Brian Everitt,et al.  Cluster analysis , 1974 .

[20]  Selim Aksoy,et al.  Kentsel yapıların biçimbilimsel bölütlenmesi , 2007 .

[21]  Huseyin Gokhan Akcay,et al.  Automated detection of objects using multiple hierarchical segmentations , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[22]  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.

[23]  Pierre Soille,et al.  Morphological Image Analysis , 1999 .

[24]  David G. Stork,et al.  Pattern Classification , 1973 .

[25]  Hichem Sahli,et al.  A hierarchical Markovian model for multiscale region-based classification of vector-valued images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Selim Aksoy,et al.  Spatial Techniques for Image Classification , 2006 .

[27]  Kristel Michielsen,et al.  Morphological image analysis , 2000 .