Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm  

作　　者：M.Rajakani R.J.Kavitha A.Ramachandran 

机构地区：[1]Full Time Research Scholar,Department of Electronics and Communication Engineering,University College of Engineering,Panruti,607106,India [2]Department of Electronics and Communication Engineering,University College of Engineering,Panruti,607106,India [3]Department of Computer Science and Engineering,University College of Engineering,Panruti,607106,India

出　　处：《Computer Systems Science & Engineering》2023年第1期265-280,共16页计算机系统科学与工程（英文）

摘　　要：Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

关 键 词：Multispectral image modifiedfirefly algorithm 3-D feature extraction feature selection multiclass support vector machine CLASSIFICATION 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]