收稿：2018-01-31，
录用：2018-3-26，
纸质出版：2018-09
移动端阅览
灾害发生以后，悬浮物质、波浪和水深等“同物异谱”会导致水体信息提取不完整，而阴影、沥青路面、浓密植被等“异物同谱”也会降低水体信息提取精度。针对此问题，提出一种特征知识引导的灾后水体信息提取方法。首先，分析水体遥感图像特征(光谱特征、几何特征、纹理特征、空间关系特征)，构建灾后水体信息提取知识决策树；其次，对遥感图像进行面向对象分割，获取对象基元，并计算特征参数；然后，在知识决策树的支持下提取灾后水体信息；最后，对水体信息提取结果进行后处理，以去除噪声和填充孔洞。选取“5·12汶川地震”遥感图像开展验证实验，结果表明，水体信息提取结果位置准确，边界清晰，生产者精度和用户精度分别为0.85和0.94，该方法能够有效提取复杂背景下的灾后水体信息。
After a disaster
the same object with different spectra (such as suspended material
wave
and water depth
each of them having more than one spectrum) leads to incomplete extraction of water body information. Different objects with the same spectrum
such as shadow
asphalt pavement
and dense vegetation
decrease the extraction precision of water body information. To address this problem
the extraction method of water body information based on its characteristics after a disaster is presented in this study. First
the characteristics (i.e.
spectra
geometry
texture
and spatial relationship) of water body information are analyzed on remote sensing images to construct a knowledge decision tree. Second
object-oriented segmentation is performed on remote sensing images to obtain object elements and calculate the characteristic parameters. Third
water body information after a disaster is extracted with the support of the knowledge decision tree. Finally
the water body information is post-processed. A validation experiment was performed using the high-spatial resolution remote sensing images of “5.12 Wenchuan Earthquake.” The location of the extracted water is accurate and the boundary is clear. The accuracies of the producer and the user are 0.85 and 0.94
respectively. Results showed that the method can effectively extract water body information after a disaster even when the background is complex.
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