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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Contextual clustering for image labeling: an application to degraded forest assessment in Landsat TM images of the Brazilian Amazon
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Contextual clustering for image labeling: an application to degraded forest assessment in Landsat TM images of the Brazilian Amazon

机译：图像标签的上下文聚类：巴西亚马逊Landsat TM图像中用于退化森林评估的应用程序

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The modified adaptive pappas clustering (MPAC) algorithm, previously published in the image processing literature, is proposed as a valuable tool in the analysis of remotely sensed images where texture information is negligible. Owing to its contextual, adaptive, and multiresolutional labeling approach, MPAC preserves genuine but small regions, is easy to use (i.e., it requires minor user interaction to run), and is robust to changes in input parameters. As an application example, an MPAC-based three-stage classifier is applied to degraded forest detection in Landsat Thematic Mapper (TM) scenes of the Brazilian Amazon, where intermediate states of forest alterations caused by anthropogenic activities can be characterized by image structures 1-3 pixels wide. In three TM images of the Para test site, where classification results are validated by means of qualitative and quantitative comparisons with aerial photos, degraded forest areas cover 13% to 45% of the image ground coverage. In the Mato Grosso test site, the degraded forest class overlaps with 1) 10% of the closed-canopy forest detected by the deforestation mapping program of the Food and Agriculture Organization (FAO, 1992), and 2) 19% of the closed-canopy forest detected by the Tropical Rain Forest Information Center (TRFIC, 1996). These figures are in line with the conclusions of a study where present estimates of annual deforestation for the Brazilian Amazon are speculated to capture less than half of the forest area that is actually impoverished each year.
机译：以前在图像处理文献中发表过的改进的自适应pappas聚类（MPAC）算法被认为是分析纹理信息可忽略的遥感图像的有价值的工具。由于其上下文，自适应和多分辨率标记方法，MPAC保留了真实但面积很小的区域，易于使用（即需要少量用户交互才能运行），并且对输入参数的更改具有鲁棒性。作为一个应用示例，基于MPAC的三级分类器应用于巴西亚马逊Landsat Thematic Mapper（TM）场景中的退化森林检测，在该场景中，由人为活动引起的森林变化的中间状态可以通过图像结构来表征1 3像素宽。在Para测试地点的三张TM图像中，通过与航拍照片进行定性和定量比较验证了分类结果，退化的森林面积覆盖了图像地面覆盖的13％至45％。在马托格罗索州试验场中，退化的森林类别与1）重叠（通过粮食及农业组织的森林砍伐测绘计划（粮农组织，1992年）检测到的封闭冠层森林的比例为10％，以及2）19％的封闭森林的比例-热带雨林信息中心（TRFIC，1996）发现的冠层森林。这些数字与一项研究的结论相符，在该研究中，目前对巴西亚马逊地区每年森林砍伐的估计被推测为仅占不到每年实际贫困的森林面积的一半。

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