A Probability-Based Improved Binary Encoding Algorithm for Classification of Hyperspectral Images

This paper presents a probability-based improved binary encoding algorithm (PIBE) for classification of hyperspectral imagery. In the proposed PIBE method, the spectral, texture and shape information from hyperspectral images as well as height information from digital elevation models (if available) are combined to form a binary code. Based on this, a probability-based approach is further introduced to match the constructed binary code to the corresponding one obtain from target classes (or training data set). Some experiments on a pair of hyperspectral images confirm the effectiveness of the proposed PIBE method.

[1]  V. Karathanassi,et al.  A texture-based classification method for classifying built areas according to their density , 2000 .

[2]  Rob J. Dekker,et al.  Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands , 2003, IEEE Trans. Geosci. Remote. Sens..

[3]  Peter Strobl,et al.  HySens-DAIS/ROSIS Imaging Spectrometers at DLR , 2002, Remote Sensing.

[4]  A. Mazer,et al.  Image processing software for imaging spectrometry data analysis , 1988 .

[5]  Nicholas J. Redding,et al.  Implementation of a Fast Algorithm for Segmenting SAR Imagery , 2002 .

[6]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[7]  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.

[8]  Emmanuel Arzuaga-Cruz,et al.  Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Liangpei Zhang,et al.  An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[10]  O. Dikshit,et al.  Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh , 2001 .

[11]  Liangpei Zhang,et al.  On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[12]  John A. Richards,et al.  Binary coding of imaging spectrometer data for fast spectral matching and classification , 1993 .

[13]  Lorenzo Bruzzone,et al.  Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[14]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[15]  Aleksandra Pizurica,et al.  Classification of Hyperspectral Data Over Urban Areas Using Directional Morphological Profiles and Semi-Supervised Feature Extraction , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Uwe Soergel,et al.  A new binary encoding algorithm for the simultaneous region-based classification of hyperspectral data and digital surface models , 2011 .

[17]  Joseph Revelli,et al.  The Image Processing Handbook, 4th Edition , 2003, J. Electronic Imaging.

[18]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[19]  Bjarne Stroustrup,et al.  The C++ programming language (2nd ed.) , 1991 .

[20]  R. Jenssen,et al.  1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .

[21]  Mohan S. Kankanhalli,et al.  Shape Measures for Content Based Image Retrieval: A Comparison , 1997, Inf. Process. Manag..

[22]  LinLin Shen,et al.  Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Andrea Baraldi,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[24]  John C. Russ,et al.  The image processing handbook (3. ed.) , 1995 .

[25]  R. Manmatha,et al.  Learning Shapes for Image Classification and Retrieval , 2005, CIVR.

[26]  Andrew J. Viterbi,et al.  Principles of Digital Communication and Coding , 1979 .