Regularized logistic regression method for change detection in multispectral data via Pathwise Coordinate optimization

Remotely sensed data by sensors on satellite or airborne platform, is becoming more and more important in monitoring the local, regional and global resources and environment. In this paper, we utilize the regularized logistic regression model for change detection of large scale remotely sensed bi-temporal multispectral images. Change detection methods base on classification schemes under this kind of condition should put more emphasis on the model's simplicity and efficiency in addition to the detection accuracy. The simple linear classifier is solved by recent proposed “Pathwise Coordinate Descent”. When applied on the L1-regularized regression problem, the algorithm can handle large problems in a comparatively very low timing cost. Through computing the solutions for a decreasing sequence of regularization parameters, the algorithm also combines model selection procedure into itself. We experiment the logistic regression with elastic-net convex penalty. Experimental results from a real data set demonstrate that, models obtained by Pathwise Coordinate Descent algorithm only need very low computational costs. The achieved remarkable efficiency indicates that regularized logistic regression via Pathwise Coordinate Descent is a promising method for large scale change detection problem in remote sensing.