We have developed, with Russell Warren, a method for HS image data that requires neither pure pixels nor any prior information about the image other than an estimate of the number of different materials present. We use nonlinear optimization and estimate the spectral and spatial structure through a relatively simple noise-tolerant algorithm. This removes the need for separate end member and spatial abundance steps. The algorithm is illustrated on real data.