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Kalman filter-based linear mixing method (KFLM) approach has shown success in Magnetic Resonance image classification. It employs an auxiliary equation, called abundance state equation (ASE), to trace the signature abundance. The signature abundance can be estimated and updated recursively by the Kalman filter and an abrupt change in signature abundance can be detected via the abundance state equation.Therefore, it requires a complete knowledge of the desired target signature and the signatures unwanted present in images.In this paper, an unsupervised kalman filter-based linear mixing method (UKFLM)approach, this didn't know how many target signatures were present in the image and where are these Signatures. UKFLM comprises two processes Target Generation Process (TGP) and Target Classification Process (TCP). The abjective of TGP is to generate aset of potential target signatures from an unknown background, which will be subsequently classified by TCP. As a result, UKFLM can be used to search for a specific target in unknown scenes. Finally, the effectiveness of UKFLM in target detection and classification is evaluated by several MR images experiments. The method has been evaluated through several experiments. All experiments were under supervision of the expert radiologist. Results show that the UKFLM have the capability for multispectral images segmentation and robustness to the noise indicating the possible usefulness of this method.