基于DWT-SVD影像处理的河流骨架线提取
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摘要
河流骨架线是一种重要的基础参考数据,有着十分重要的意义。目前,提取河流骨架线的方法常常会出现断裂,植被等非水体信息也没有得到有效的抑制。本文提出利用DWT-SVD对低反差原始影像进行增强处理,提高NDWI和INDVI的特征提取精度,利用INDVI对NDWI进行非水体信息影响抑制和水体信息增益,使得水体信息提取更为准确,利用数学形态学约束河流二值图像,最终提取河流骨架线。实验表明,该方法提取的河流骨架线更符合河流的真实位置与形状,具有更高的准确性。
River skeleton line was an important basic reference data,which had the great significance. At present,the method of extracting river skeleton line often appeared fracture,and the non-water information such as vegetation had not been effectively suppressed. In order to improve the accuracy of NDWI and INDVI feature extraction,DWT-SVD was used to enhance the original image with low contrast. INDVI was used to suppress the influence of non-water body information on NDWI and increased the information gain of water body,which made the extraction of water body information more accurate. Using mathematical morphology to constrain river binary images,and finally river skeleton lines was extracted. Experiments showed that the skeleton line extracted by this method was more met the true position and shape of the river and had higher accuracy.
River skeleton line was an important basic reference data,which had the great significance. At present,the method of extracting river skeleton line often appeared fracture,and the non-water information such as vegetation had not been effectively suppressed. In order to improve the accuracy of NDWI and INDVI feature extraction,DWT-SVD was used to enhance the original image with low contrast. INDVI was used to suppress the influence of non-water body information on NDWI and increased the information gain of water body,which made the extraction of water body information more accurate. Using mathematical morphology to constrain river binary images,and finally river skeleton lines was extracted. Experiments showed that the skeleton line extracted by this method was more met the true position and shape of the river and had higher accuracy.
引文
[1] A. K. Bhandari&A. Kumar&G. K. Singh. Improved feature extraction scheme for satellite images using NDVI and NDWI technique based on DWT and SVD. Arab J Geosci. 2014(11).
[2] Kazi Rifat Ahmed,Simu Akter. Analysis of landcover change in southwest Bengal delta due to floods by NDVI,NDWI and K-means cluster with landsat multi-spectral surface reflectance satellite data. Remote Sensing Applications:Society and Environment,2017(8):168–181.
[3]刘小磊,覃志豪. NDWI与NDVI指数在区域干旱监测中的比较分析———以2003年江西夏季干旱为例[J].遥感技术与应用,2007(5):608-612.
[4]孟令奎,李珏,王锐,等.数学形态学与拓扑约束支持的单条河流骨架线提取[J].遥感学报,2017,21(5):785-795.
[5]陈树辉,刘忠华,刘翔,等.基于TM遥感影像的南京市水体信息提取[J].安徽农业科学,2011,39(11):6863-6876.
[6]赵威成,祁向前,叶欣,等.基于兰氏距离NDVI的土地覆盖变化检测[J].安徽农业科学,2017(6):221-223.
[7]刘小凤,吴艳兰,胡海.面状要素的多层次骨架线提取[J].测绘学报,2013,42(4):588-594.
[8]骆剑承,盛永伟,沈占锋,等.分步迭代的多光谱遥感水体信息高精度自动提取[J].遥感学报,2009,13(4):610-615.
[9]马惠萍,程涛,张磊,等.基于Landsat TM影像的兰州市土地覆盖变化检测[J].矿山测量,2017,45(5):57-59,67.
[2] Kazi Rifat Ahmed,Simu Akter. Analysis of landcover change in southwest Bengal delta due to floods by NDVI,NDWI and K-means cluster with landsat multi-spectral surface reflectance satellite data. Remote Sensing Applications:Society and Environment,2017(8):168–181.
[3]刘小磊,覃志豪. NDWI与NDVI指数在区域干旱监测中的比较分析———以2003年江西夏季干旱为例[J].遥感技术与应用,2007(5):608-612.
[4]孟令奎,李珏,王锐,等.数学形态学与拓扑约束支持的单条河流骨架线提取[J].遥感学报,2017,21(5):785-795.
[5]陈树辉,刘忠华,刘翔,等.基于TM遥感影像的南京市水体信息提取[J].安徽农业科学,2011,39(11):6863-6876.
[6]赵威成,祁向前,叶欣,等.基于兰氏距离NDVI的土地覆盖变化检测[J].安徽农业科学,2017(6):221-223.
[7]刘小凤,吴艳兰,胡海.面状要素的多层次骨架线提取[J].测绘学报,2013,42(4):588-594.
[8]骆剑承,盛永伟,沈占锋,等.分步迭代的多光谱遥感水体信息高精度自动提取[J].遥感学报,2009,13(4):610-615.
[9]马惠萍,程涛,张磊,等.基于Landsat TM影像的兰州市土地覆盖变化检测[J].矿山测量,2017,45(5):57-59,67.