An artificial immune network approach to multi-sensor land use/land cover classification

An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification. Major improvements of OPTINC compared to a typical immune network-based classification model (aiNet) include (1) preservation of the best antibodies of each land cover class from the antibody population suppression, which ensures that each land cover class is represented by at least one antibody; (2) mutation rates being self-adaptive according to the model performance between training generations, which improves the model convergence; and (3) incorporation of both Euclidean distance and spectral angle mapping distance to measure affinity between two feature vectors using a genetic algorithm-based optimization, which helps the model to better discriminate LULC classes with similar characteristics. OPTINC was evaluated using two sites with different remote sensing data: a residential area in Denver, CO with high-spatial resolution QuickBird image and LiDAR data, and a suburban area in Monticello, UT with HyMap hyperspectral imagery. A decision tree, a multilayer feed-forward back-propagation neural network, and aiNet were also tested for comparison. Classification accuracy, local homogeneity of classified images, and model sensitivity to training sample size were examined. OPTINC outperformed the other models with higher accuracy and more spatially cohesive land cover classes with limited salt-and-pepper noise. OPTINC was relatively less sensitive to training sample size than the neural network, followed by the decision tree.

[1]  Mahesh Pal,et al.  Artificial immune‐based supervised classifier for land‐cover classification , 2008 .

[2]  Liangpei Zhang,et al.  A Supervised Artificial Immune Classifier for Remote-Sensing Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Leandro Nunes de Castro,et al.  aiNet: An Artificial Immune Network for Data Analysis , 2002 .

[4]  Daniel A. Keim,et al.  Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , 2002, KDD.

[5]  J. R. Jensen,et al.  A Genetic Algorithm Approach to Moving Threshold Optimization for Binary Change Detection , 2011 .

[6]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[8]  Dipankar Dasgupta,et al.  A biologically inspired password authentication system , 2009, CSIIRW '09.

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  Taşkin Kavzoĝlu,et al.  An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images , 2001 .

[11]  J. V. Bradley.  Distribution-Free Statistical Tests , 1968 .

[12]  Edit J. Kaminsky,et al.  Neural network classification of remote-sensing data , 1995 .

[13]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[14]  Jonathan Timmis.  Artificial immune systems : a novel data analysis technique inspired by the immune network theory , 2000 .

[15]  Fernando José Von Zuben,et al.  An Evolutionary Immune Network for Data Clustering , 2000, SBRN.

[16]  Giorgos Mountrakis,et al.  International Journal of Remote Sensing , 2022 .

[17]  Jianwen Ma,et al.  Land cover classification from MODIS EVI times-series data using SOM neural network , 2005 .

[18]  Peter F. Fisher,et al.  Remote sensing of land cover classes as type 2 fuzzy sets , 2010 .

[19]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[20]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[21]  O. Hagner,et al.  A method for calibrated maximum likelihood classification of forest types , 2007 .

[22]  M. Hodgson,et al.  Object-Based Land Cover Classification Using High-Posting-Density LiDAR Data , 2008 .

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[25]  Taskin Kavzoglu,et al.  A kernel functions analysis for support vector machines for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[27]  Barnali M. Dixon,et al.  Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? , 2008 .

[28]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[29]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[30]  F. Burnet.  The clonal selection theory of acquired immunity , 1959 .

[31]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[32]  Giorgos Mountrakis,et al.  Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example , 2010 .

[33]  V. Devarajan,et al.  Artificial immune systems and aerial image segmentation , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[34]  Vincenzo Cutello,et al.  Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials , 2005, ICARIS.

[35]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[36]  Taskin Kavzoglu,et al.  Increasing the accuracy of neural network classification using refined training data , 2009, Environ. Model. Softw..

[37]  Jean-Philippe Gastellu-Etchegorry,et al.  A model-based performance test for forest classifiers on remote-sensing imagery , 2009 .

[38]  Ujjwal Maulik,et al.  Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[39]  Ujjwal Maulik,et al.  A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification , 2005, Fuzzy Sets Syst..

[40]  John R. Jensen,et al.  Opening the black box of neural networks for remote sensing image classification , 2004 .

[41]  A. Agresti.  An introduction to categorical data analysis , 1997 .

[42]  Shiv O. Prasher,et al.  Measuring performance in precision agriculture: CART-A decision tree approach , 2006 .

[43]  Li Li,et al.  An Artificial Immune Approach for Vehicle Detection from High Resolution Space Imagery , 2007 .

[44]  Ming-Hseng Tseng,et al.  A genetic algorithm rule-based approach for land-cover classification , 2008 .

[45]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[46]  Geneva G. Belford,et al.  Instability of decision tree classification algorithms , 2001, KDD.

[47]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[48]  Thomas Stibor,et al.  An Empirical Study of Self/Non-self Discrimination in Binary Data with a Kernel Estimator , 2008, ICARIS.

[49]  Lei Zhang,et al.  A resource limited artificial immune system algorithm for supervised classification of multi/hyper‐spectral remote sensing imagery , 2007 .