Abstract: Recently collaborative representation classification (CRC) for hyperspectral image analysis attract increasing attentions. The existing related algorithms can't distinguish classes efficiently because of information redundancy of the hyperspectral data. The local manifold structure can significantly enhance distinguishing between the classes and handle the nonlinear problems efficiently. To apply local manifold structure to CRC, a new CRC in tangent space and an adaptive weighted CRC method in tangent space based on the Euclidean distance are proposed. In order to approximate the local manifold of testing samples, the proposed method uses CRC in tangent space to find the best linearly representational approximation between testing sample and training sample. Furthermore, adaptive weighted diagonal matrices are adopted in the proposed method, which constituted by the Euclidean distances between testing samples and training samples, testing samples and neighbor samples respectively. In the experiments, two real hyperspectral images collected by different sensors were adopted for performance evaluations, and experimental results show that TCRC and WTCRC have significantly improved classification performance compared with the state-of-art SVM and other CR-based classifiers.