Due to the heavy computational complexity, the efficiency of FFBP algorithm descends obviously. Thus, an approximate FFBP algorithm is proposed in this paper. With the imaging plane on the sin r coordinate system, there is no need to calculate the arcsines of the back-projected angle. Meanwhile, the polynomial estimation method is introduced to obtain the approximate values of back-projected slant range and angle. As a result, the number of operations is further reduced. A detailed analysis of computation burden is given as well. At the end of the paper, dealing with the data of different wavebands, the simulation result of the point targets shows that the algorithm proposed is accurately and effective to give well focused images.

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The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis, abnormal removal and background filling are performed to obtain a purified background. Finally, the purified background and the original hyperspectral image are used for joint collaborative representation to complete the detection. Experimental results show that the detection accuracy of the algorithm proposed in this paper was efficiently improved by introducing multiple feature joint trilateral filtering, where multiple spatial spectrum features are utilized.