Unsupervised linear unmixing Kalman filtering approach to signature extraction and estimation for remotely sensed imagery

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Abstract
Linear Unmixing Kalman Filtering (LUKF) approach was recently developed which incorporates the concept of linear unmixing into Kalman filtering so as to achieve signature abundance estimation, subpixel detection and classification for remotely sensed images. However, LUKF assumes a complete knowledge of the signature matrix used in the linear mixture model. In this paper, the LUKF is extended to an unsupervised LUKF where no knowledge about the signature matrix is required a priori. The unsupervised learning method proposed for the ULUKF is derived from a vector quantization-based clustering algorithm. It employs a nearest-neighbor rule to group potential signatures resident within an image scene into a class of distinct clusters whose centers represent different types of signatures. These clusters' centers are then used as if they were true signatures in the signature matrix LUKF. In order to evaluate the effectiveness of ULUKF, HYDICE images were used for assessment. The results produced by ULUKF show that subpixel detection and classification can be performed.

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