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.

高光谱图像中的一项分析是目标检测。随着最近的发展和高空间分辨率图像的创建，在目标检测中使用光谱和空间信息的需求已经增加。最近，已经提出了一种使用最小生成森林（MSF）算法进行光谱和空间分类的有效方法。试图改进这种方法用于高光谱图像中的目标检测。在所提出的方法中，光谱图像主要使用分水岭算法进行分割。然后，对于分割后的对象，提取了面积、环境、强度、意义强度和熵五个空间属性。最后，利用基于标记的MSF算法进行检测操作。上述技术应用于三个高光谱图像，第一个图卢兹，第二个图卢兹和魁北克。定量和定性评估结果表明，与图卢兹第一幅、第二幅图卢兹和魁北克图像中的光谱角测量（SAM）算法相比，所提出的方法分别将 kappa 系数提高了 40%、34% 和 23%。