Semi-supervised change detection using modified self-organizing feature map neural network

• A semi-supervised change detection method is proposed using modified self-organizing feature map.
• Training of the self-organizing feature map is initially performed using a few labeled patterns.
• The membership of each unlabeled pattern is determined using the concept of fuzzy set theory.
• A heuristic method has been suggested to select some patterns from the unlabeled ones for training.
• Iterative learning is carried out using the labeled and selected unlabeled patterns.

In the present article, semi-supervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In the proposed methodology, training of the MSOFM network is initially performed using only a few labeled patterns. Thereafter, the membership values, in both the classes, for each unlabeled pattern are determined using the concept of fuzzy set theory. The soft class label for each of the unlabeled patterns is then estimated using the membership values of its K nearest neighbors. Here, training of the network using the unlabeled patterns along with a few labeled patterns is carried out iteratively. A heuristic method has been suggested to select some patterns from the unlabeled ones for training. To check the effectiveness of the proposed methodology, experiments are conducted on three multi-temporal and multi-spectral data sets. Performance of the proposed work is compared with that of two unsupervised techniques, a supervised technique and two semi-supervised techniques. Results are also statistically validated using paired t-test. The proposed method produced promising results.

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