INDIVIDUAL TREE CROWNS DELINEATION USING LOCAL MAXIMA APPROACH AND SEEDED REGION GROWING TECHNIQUE
INDIVIDUAL TREE CROWNS DELINEATION USING LOCAL MAXIMA APPROACH AND SEEDED REGION GROWING TECHNIQUE
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发表时间：
2011
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通讯作者：
J. Novotný
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作者：
J. Novotný
遥感在林业中的应用可得益于光学传感器的迅速发展。新的高光谱传感器具有非常高的空间和光谱分辨率，并在可见光和红外光谱区提供连续的光谱覆盖。应用的算法应该适合于数据的新属性，以实现其最大的优势。将图像分割成目标是图像处理中的一项基本任务。这对光学遥感在林业上的应用也有重要意义。我们要找的是单个树冠的位置。这种过程传统上涉及两个部分- 1）检测和2）描绘阶段。提出了局部极大值法和种子区域生长技术。此外，改进，即直方图均衡化和Voronoi图，纳入。两个独立的数据集进行了处理和分割的结果。空间分辨率为0.8米的高光谱数据被认为是一个合适的分割过程中，84%和78%的检测阶段和64%和52%的准确度在划定阶段分别。最后根据分割结果讨论了算法中的推荐设置。
Remote sensing applications in forestry can profit from a rapid development of optical sensors. New hyperspectral sensors have very high spatial and spectral resolution and provide continuous spectral cover in visible and infrared spectral region. Applied algorithms should be suited to the new properties of the data to achieve its maximal advantage. Segmentation of the image into objects is a fundamental task in image processing. It is important in forestry applications of optical remote sensing as well. We are looking for a position of individual tree crowns. Such process traditionally involves two parts — 1) detection and 2) delineation phase. Local maxima approach and seeded region growing technique are presented as the key concepts. Furthermore improvements, namely histogram equalization and Voronoi diagram, are incorporated. Two independent datasets were processed and results of the segmentation are presented. Hyperspectral data with spatial resolution of 0.8m were found as a suitable for segmentation process with 84% and 78% accuracy of detection phase and 64% and 52% accuracy in delineation phase respectively. Finally discussion of recommended settings in the algorithm is provided based on the segmentation results.