Classification of hyperspectral remote sensing imagery by k-nearest-neighbor simplex based on adaptive C-mutual proportion standard deviation metric

Abstract The k-nearest-neighbor simplex (kNNS) based on an adaptive C-mutual proportion standard deviation metric for classification of hyperspectral remote sensing imagery was proposed. By analyzing spectral characteristics on the samples of the same and different classes, a C-mutual proportion standard deviation metric is put forward which innovates a novel metric on distance and similarity measures for pattern recognition. Combined with the adaptive adjusting algorithm, this metric is used for the classification of hyperspectral remote sensing imagery. The traditional kNNS classification algorithm is improved by this metric and the adaptive adjusting algorithm, and its classification accuracy is enhanced. Three experiments with different types of hyperspectral imagery are conducted to evaluate the performance of the proposed algorithm in comparison to the other five classification algorithms. The experimental results demonstrate that the proposed algorithm is superior to other algorithms on overall accuracy and kappa coefficient.

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

[2]  Pao-Ta Yu,et al.  A Nonparametric Feature Extraction and Its Application to Nearest Neighbor Classification for Hyperspectral Image Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Lei Guo,et al.  Improving the classification precision of spectral angle mapper algorithm , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[4]  Trac D. Tran,et al.  Hyperspectral Image Classification Using Dictionary-Based Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Hongwei Zhu,et al.  An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification and Regression , 1995, NIPS.

[7]  Jason Weston,et al.  Semisupervised Neural Networks for Efficient Hyperspectral Image Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Farid Melgani,et al.  Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Alfred Stein,et al.  Incorporating Uncertainty via Hierarchical Classification Using Fuzzy Decision Trees , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Amir F. Atiya,et al.  Estimating the Posterior Probabilities Using the K-Nearest Neighbor Rule , 2005, Neural Computation.

[11]  Shuicheng Yan,et al.  Classification and Feature Extraction by Simplexization , 2008, IEEE Transactions on Information Forensics and Security.

[12]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[13]  Luis Samaniego,et al.  Supervised Classification of Remotely Sensed Imagery Using a Modified $k$-NN Technique , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[14]  N. Coops,et al.  Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. , 2009 .

[15]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[16]  Shuicheng Yan,et al.  Discriminant simplex analysis , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[18]  Liang Shi,et al.  [Classification of hyperspectral imagery based on ant colony compositely optimizing SVM in spatial and spectral features]. , 2013, Guang pu xue yu guang pu fen xi = Guang pu.

[19]  Jungho Im,et al.  An artificial immune network approach to multi-sensor land use/land cover classification , 2011 .

[20]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[21]  Juan J. Flores,et al.  The application of artificial neural networks to the analysis of remotely sensed data , 2008 .

[22]  Karol Kozak,et al.  Weighted k-Nearest-Neighbor Techniques for High Throughput Screening Data , 2007 .

[23]  Jinwen Tian,et al.  Generalised supervised local tangent space alignment for hyperspectral image classification , 2010 .

[24]  Goo Jun,et al.  Spatially Adaptive Classification of Land Cover With Remote Sensing Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[25]  D. D. Ridder,et al.  Locally linear embedding for classification , 2002 .

[26]  Dimitrios Gunopulos,et al.  Locally Adaptive Metric Nearest-Neighbor Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Hongyu Li,et al.  Supervised Learning on Local Tangent Space , 2005, ISNN.

[28]  Lei Zhang,et al.  Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.