Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral unmixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel. To be physically meaningful, LSMM is subject to two constraints: the first constraint requires all abundances to be nonnegative and the second one requires all abundances to be summed to one. Independent component analysis (ICA) has been proposed as an advanced tool to unmix hyperspectral image. However, ICA is based on the assumption of mutually independent sources, which violates the constraint conditions in LSMM. This embarrassment compromises ICA applicability to hyperspectral data. To overcome this problem, the present paper introduces a solution of minimization of total correlation of the components. Interestingly, with the minimization of total correlation of the components, the angle of the direction between each components is invariable. A Parallel oblique-ICA (Pob-ICA) algorithm is proposed to correct the angle of the searching direction between the components. Two novelties result from our proposed Pob-ICA algorithm. First, the algorithm completely satisfies the physical constraint conditions in LSMM and overcomes the limitation of statistical independency assumed by ICA. Second, the last component, which is missed in other existing ICA algorithms, can be estimated by our proposed algorithm. In experiments, Pob-ICA algorithm demonstrates excellent performance in the simulative and real hyperspectral images.