Learning to Combine Spectral Indices with Genetic Programming

This paper introduces a Genetic Programming-based method for band selection and combination, aiming to support remote sensing image classification tasks. Relying on ground-truth data, our method selects spectral bands and finds the arithmetic combination of those bands (i.e., spectral index) that best separates examples of different classes. Experimental results demonstrate that the proposed method is very effective in pixel-wise binary classification problems.

[1]  Feifei Xu,et al.  Unsupervised Hyperspectral Band Selection by Dominant Set Extraction , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Mingyi He,et al.  Band selection based on feature weighting for classification of hyperspectral data , 2005, IEEE Geoscience and Remote Sensing Letters.

[3]  Qian Du,et al.  Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis , 2008, IEEE Geoscience and Remote Sensing Letters.

[4]  Jason M. Daida,et al.  Classification of spectral imagery using genetic programming , 2000 .

[5]  Neal R. Harvey,et al.  GENIE: a hybrid genetic algorithm for feature classification in multispectral images , 2000, SPIE Optics + Photonics.

[6]  Brian J. Ross,et al.  Hyperspectral image analysis using genetic programming , 2002, Appl. Soft Comput..

[7]  LinLin Shen,et al.  Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[9]  Lorenzo Bruzzone,et al.  Hyperspectral Band Selection Based on Rough Set , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Yongchao Zhao,et al.  A Fast Volume-Gradient-Based Band Selection Method for Hyperspectral Image , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Cyril Fonlupt,et al.  Genetic Programming with Dynamic Fitness for a Remote Sensing Application , 2000, PPSN.

[12]  P. Groves,et al.  Methodology For Hyperspectral Band Selection , 2004 .

[13]  Qingquan Li,et al.  A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Jefersson Alex dos Santos,et al.  Efficient Unsupervised Band Selection Through Spectral Rhythms , 2015, IEEE Journal of Selected Topics in Signal Processing.

[15]  Xin-She Yang,et al.  Nature-Inspired Framework for Hyperspectral Band Selection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Qi Wang,et al.  Hyperspectral Band Selection by Multitask Sparsity Pursuit , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Edward A. Fox,et al.  A genetic programming framework for content-based image retrieval , 2009, Pattern Recognit..

[18]  Robert I. Damper,et al.  Band Selection for Hyperspectral Image Classification Using Mutual Information , 2006, IEEE Geoscience and Remote Sensing Letters.

[19]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[20]  J. Kittler,et al.  Feature Set Search Alborithms , 1978 .

[21]  Alejandro Hinojosa-Corona,et al.  A Genetic Programming Approach to Estimate Vegetation Cover in the Context of Soil Erosion Assessment , 2011 .

[22]  Jacques-André Landry,et al.  A Genetic-Programming-Based Method for Hyperspectral Data Information Extraction: Agricultural Applications , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Mohamed Cheriet,et al.  Hyperspectral band selection based on graph clustering , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[24]  Michael W. Prairie,et al.  Visual method for spectral band selection , 2004, IEEE Geoscience and Remote Sensing Letters.

[25]  Qian Du,et al.  An Efficient Method for Supervised Hyperspectral Band Selection , 2011, IEEE Geoscience and Remote Sensing Letters.

[26]  Peter Bajcsy,et al.  Methodology for hyperspectral band and classification model selection , 2003, IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003.

[27]  Jee Cheng Wu,et al.  Unsupervised Cluster-based Band Selection for Hyperspectral Image Classification , 2013 .