基于高斯差分模型的雪地扰动痕迹遥感识别

雪面上人或动物的活动信息涉及冬季动物迁移追踪、人员活动轨迹检测、安全防卫等各领域应用,利用遥感技术监测人或动物的活动痕迹可以及时掌握动物的迁移情况、江上人员活动范围等信息,进而可为相应的管理决策提供依据。利用高分辨率遥感技术,通过图像滤波等增强处理,可以识别冰雪覆盖区域经人或动物活动后产生的微弱线状扰动痕迹,为后续分析提供帮助。提出了利用高斯差分滤波器(DoG)进行冬季冰雪覆盖区域扰动痕迹识别的监测方法。通过对吉林省龙井市结冰江面上活动痕迹的实验表明,当σ取1.5时,DoG带通滤波频率与人员过江痕迹频率一致,对痕迹增强效果最明显,此时的滤波器为最佳滤波器,痕迹总体提取精度达到83.67%,优于传统的Laplacian算子、Sobel算子和Prewitt算子滤波方法。说明通过DoG滤波器处理,能够有效地突出雪面上人或动物的活动痕迹,可为进一步识别人或动物活动类型、追踪路线以及相关部门的安全巡逻提供服务。

Recognition of Activity Tracks in the Snow with Remote Sensing based on Difference of Gaussian Filter

Human or animal activity information on snow relates to applications in various fields animal tracking such as,in winter,animal migration tracking,public safety and defense,etc.Remote sensing technologies can play key roles in monitoring the tracks of human or animal activity traces through image enhancement,e.g.filter techniques with very high spatial resolution images,especially linear disturbance traces from human or animal activities in snow\|covered area,and grasp the animal migration,human activities on the frozen river and other information timely to further assist in decision\|making.In this paper the principle and characteristics of the Difference of Gaussian filter(DoG) were proposed to identify linear disturbance traces in the snow,and experiments showed that the accuracy of traces extraction reached 83.67% with an adaptive filter(σ=1.5).The results were more reliable and effective than that from laplacian,sobel and prewitt operators.DoG filter is more suitable for human activity traces identification on the frozen river,and will play a key role in further services for recognizing human or animal activity type,trace route,as well as security patrols of relevant departments.
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