To solve the problem of adaptive detection for distributed targets in partially homogeneous environment with outliers and limited samples, a class of adaptive detectors are designed based on geometric median in this paper. The first step is to construct a data selector based on geometric median generalized inner product and eliminate sample data containing outliers. The second step is to construct detection statistics of the generalized adaptive subspace detector using covariance matrix estimators, which are based on geometric median. The detectors utilize geometric median of the positive definite matrix space without any knowledge of prior probability distribution of sample data. The performance of the proposed two-step detectors is evaluated in terms of the probabilities of correct outliers excision, false alarm, and detection. Experiment results based on simulated and real data show that the proposed approach has better detection performance than the existing ones based on traditional covariance estimator.