Segmentation of multispectral remote sensing images using active support vector machines
The problem of scarcity of labeled pixels, required for segmentation of remotely sensed satellite images in supervised pixel classification framework, is addressed in this article. A support vector machine (SVM) is considered for classifying the pixels into different landcover types. It is initially designed using a small set of labeled points, and subsequently refined by actively querying for the labels of pixels from a pool of unlabeled data. The label of the most interesting/ambiguous unlabeled point is queried at each step. Here, active learning is exploited to minimize the number of labeled data used by the SVM classifier by several orders. These features are demonstrated on an IRS-1A four band multispectral image. Comparison with related methods is made in terms of number of data points used, computational time and a cluster quality measure.
image segmentation semi-supervised learning transductive learning query support vector machine