| Summary: | One of the analyses in hyperspectral images is target detection. With recent developments and the creation of high spatial resolution images, the need to use both spectral and spatial information in the target detection has increased. Recently, an effective approach for spectral and spatial classification has been proposed using minimum spanning forest (MSF) algorithm. It was attempted to improve this approach for target detection in hyperspectral images. In the proposed method, the spectral image was primarily segmented using the watershed algorithm. Afterwards, for the objects resulting from segmentation, five spatial properties of area, environment, strength, meaning intensity, and entropy were extracted. Finally, the detection operation was performed utilizing the marker-based MSF algorithm. The above-mentioned techniques were applied to three hyperspectral images, first Toulouse, second Toulouse and Quebec. The results of quantitative and qualitative evaluations showed that the proposed method improved the kappa coefficient by 40, 34 and 23% in comparison with the spectral angle measurement (SAM) algorithm in the first Toulouse, second Toulouse and Quebec images, respectively.
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