Supervised Segmentation of Remote Sensing Image Using Reference Descriptor

In this letter, we propose the use of a novel feature representation called reference descriptor (RD) for supervised remote sensing image segmentation. Different from traditional low-level image features such as color, shape, and texture, which are directly extracted from an image, RD describes a data sample by its similarities to the exemplar data in a reference set and is a higher level feature representation of the data sample. Experiments show that comparing with segmentation using low-level image features, RD is more robust against intraclass variation of land cover type. Using RD can improve the accuracy in a supervised segmentation (classification) framework, and superior performance is observed in comparison to other methods on different image data. In addition, compared with the competing methods, an RD-based method is more efficient.

[1]  DeLiang Wang,et al.  Remote Sensing Image Segmentation by Combining Spectral and Texture Features , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  David J. Kriegman,et al.  Pose, illumination and expression invariant pairwise face-similarity measure via Doppelgänger list comparison , 2011, 2011 International Conference on Computer Vision.

[3]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[4]  Tamás Szirányi,et al.  Segmentation of Remote Sensing Images Using Similarity-Measure-Based Fusion-MRF Model , 2014, IEEE Geoscience and Remote Sensing Letters.

[5]  Cordelia Schmid,et al.  Learning Color Names for Real-World Applications , 2009, IEEE Transactions on Image Processing.

[6]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[8]  Bir Bhanu,et al.  Reference-based person re-identification , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

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

[10]  Josiane Zerubia,et al.  Supervised segmentation of remote sensing images based on a tree-structured MRF model , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Wenzhong Shi,et al.  Classification of Very High Spatial Resolution Imagery Based on a New Pixel Shape Feature Set , 2014, IEEE Geoscience and Remote Sensing Letters.

[12]  Deren Li,et al.  Object Classification of Aerial Images With Bag-of-Visual Words , 2010, IEEE Geoscience and Remote Sensing Letters.

[13]  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.

[14]  Hye-Jin Kim,et al.  A shape–size index extraction for classification of high resolution multispectral satellite images , 2012 .

[15]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[16]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[17]  Liangpei Zhang,et al.  A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Wei Xie,et al.  Supervised Image Segmentation Based on Tree-Structured MRF Model in Wavelet Domain , 2009, IEEE Geoscience and Remote Sensing Letters.

[19]  Curt H. Davis,et al.  A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..