Abstract
Feature selection in multispectral high dimensional information is a hard labour machine learning problem because of the imbalanced classes present in the data. The existing Most of the feature selection schemes in the literature ignore the problem of class imbalance by choosing the features from the classes having more instances and avoiding significant features of the classes having less instances. In this paper, SMOTE concept is exploited to produce the required samples form minority classes. Feature selection model is formulated with the objective of reducing number of features with improved classification performance. This model is based on dimensionality reduction by opt for a subset of relevant spectral, textural and spatial features while eliminating the redundant features for the purpose of improved classification performance. Binary ALO is engaged to solve the feature selection model for optimal selection of features. The proposed ALO-SVM with wrapper concept is applied to each potential solution obtained during optimization step. The working of this methodology is tested on LANDSAT multispectral image
Highlights
Classification of remote sensing imagery is an important task for land cover image analysis
Binary Ant Lion Optimization (ALO) based feature selection with SMOTE balancing methodology is tested on the land cover data set attained from Landsat-7 imagery
This image is segmented into objects and classified into six classes namely agriculture, forest, buildup urban, buildup rural, water bodies and barren land based on SVM classifier
Summary
Classification of remote sensing imagery is an important task for land cover image analysis. The class imbalance occurs when the sample space of some classes are more whereas sample space of few other classes are less in the total sample data This unequal distribution of classes can lead to diminish the classification performance due to the absence of important features of the minority class in the feature subset. Wrapper methods are classifier dependent in which each searched subset in the iterative process is given to the classifier to evaluate the classification performance. It is computationally exhaustive procedure with more number of features. Section-IV explains the structure of binary ALO and its implementation of SMOTE and wrapper based proposed optimal feature subset selection methodology. Findings and applicability of the Binary ALO methodology are given in the concluding Section-VI
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