一種改進(jìn)的ACM算法及其在鄱陽(yáng)湖水域監(jiān)測(cè)中的應(yīng)用
doi: 10.11999/JEIT160870 cstr: 32379.14.JEIT160870
-
2.
(中科院電子學(xué)研究所 北京 100190) ②(中國(guó)科學(xué)院大學(xué) 北京 100190)
國(guó)家自然科學(xué)基金優(yōu)秀青年基金(61422113)
Improved ACM Algorithm for Poyang Lake Monitoring
-
2.
(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
The National Natural Science Fund of China for Excellent Young Scholars (61422113)
-
摘要: Sentinel-1合成孔徑雷達(dá)(SAR)衛(wèi)星具有測(cè)繪帶寬、重訪周期短、分辨率高等優(yōu)點(diǎn),為長(zhǎng)時(shí)間的水域監(jiān)測(cè)提供數(shù)據(jù)基礎(chǔ)。2016年長(zhǎng)江中下游地區(qū)洪澇災(zāi)害嚴(yán)重,鄱陽(yáng)湖是長(zhǎng)江干流的重要調(diào)蓄性湖泊之一,基于SAR圖像的鄱陽(yáng)湖水域提取及其變化檢測(cè)具有重要意義。然而受相干斑噪聲的影響,尤其是在鄱陽(yáng)湖分布較廣、地物背景較復(fù)雜、弱邊緣和模糊邊緣較多的情況下,傳統(tǒng)的水域分割方法邊緣保持性較差、提取精度較低。針對(duì)上述問題,該文提出一種基于局部窄帶的ACM邊緣提取算法,并將其應(yīng)用于Sentinel-1A獲取的鄱陽(yáng)湖水域時(shí)序觀測(cè)圖像中。該算法首先采用兩級(jí)Otsu方法獲取初始輪廓,隨后在初始輪廓附近建立局部窄帶,最后在窄帶內(nèi)采用基于區(qū)域的ACM方法進(jìn)行輪廓線演化來(lái)解決弱邊緣或模糊邊緣問題。實(shí)驗(yàn)結(jié)果表明該方法在邊緣保持和分割精度上具有明顯優(yōu)勢(shì),并且降低了計(jì)算時(shí)間。
-
關(guān)鍵詞:
- 合成孔徑雷達(dá) /
- 鄱陽(yáng)湖 /
- 局部窄帶 /
- 水域提取
Abstract: Sentinel-1 satellite constellation offers enough Synthetic Aperture Radar (SAR) images for long-term water monitoring, due to its relative large swath, great revisit frequency and high resolution. The middle and upper Yangtze River suffers serious flood disaster in 2016. It is significant to detect water and its changes of Poyang Lake, since it is one of the important flood storage lakes along the Yangtze River mainstream. However, the traditional segmentation algorithm has shortage in edge preservation and the accuracy of water detection, especially in the case of Poyang Lake, which is widely distributed and has more complex background, weak edges and blurred edges. A new Active Contour Model (ACM) algorithm based on local narrowband is proposed to solve these problems, and it is applied to Sentinel-1A observations related to Poyang Lake. First, a cascade two-level Otsu approach is adopted to obtain the initial contour. Second, the local narrowband is built along the initial contour to reduce the calculating time. Finally, a region-based ACM is introduced into the local narrowband to stop the contours at weak or blurred edges. Experiment results show that the new method has advantages in the edge preservation and obtains better segmentation results with respect to other methods.-
Key words:
- Synthetic Aperture Radar (SAR) /
- Poyang lake /
- Local narrowband /
- Water detection
-
YESOU H, HUBER C, HAOUET S, et al. Exploiting sentinel 1 time series to monitor the largest fresh water bodies in PR China, the Poyang lake[C]. IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016: 3882-3885. doi: 10.1109/IGARSS.2016.7730008. ZHANG P, FENG L, LU J Z, et al. Hydrodynamic and inundation modeling of Chinas largest freshwater lake aided by remote sensing data[J]. Remote Sensing, 2015, 7(4): 4858-4879. doi: 10.3390/rs70404858. LAI X J, SHANKMAN D, HUBER C, et al. Sand mining and increasing Poyang Lakes discharge ability: A reassessment of causes for lake decline in China[J]. Journal of Hydrology, 2014, 519(1): 1698-1706. doi: 10.1016/j.jhydrol.2014.09.058. YE X C, ZHANG Q, LIU J, et al. Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China[J]. Journal of Hydrology, 2013, 494(12): 83-95. doi: 10.1016 /j.jhydrol.2013.04.036. FENG L, HU C, CHEN X, et al. Dramatic inundation changes of Chinas two largest freshwater lakes linked to the Three Gorges Dam[J]. Environmental Science and Technology, 2013, 47(17): 9628-9634. doi: 10.1021/es4009618. FENG L, HU C M, HAN X X, et al. Long-term distribution patterns of Chlorophyll-a concentration in Chinas largest freshwater lake: MERIS full-resolution observations with a practical approach[J]. Remote Sensing, 2015, 7(1): 275-299. doi: 10.3390/rs70100275. LI L, XIA H, LI Z, et al. Temporal-spatial evolution analysis of lake size-distribution in the middle and lower Yangtze river basin using Landsat imagery data[J]. Remote Sensing, 2015, 7(8): 10364-10384. doi: 10.3390/rs70810364. 安成錦, 牛照東, 李志軍, 等. 典型 Otsu 算法閾值比較及其SAR 圖像水域分割性能分析[J]. 電子與信息學(xué)報(bào), 2010, 32(9): 2215-2219. doi: 10.3724/SP.J.1146.2009.01426. AN Chengjin, NIU Zhaodong, LI Zhijun, et al. Otsu threshold comparison and SAR water segmentation result analysis[J]. Journal of Electronics Information Technology, 2010, 32(9): 2215-2219. doi: 10.3724/SP.J.1146.2009.01426. SHENG G F, YANG W, DENG X P, et al. Coastline detection in synthetic aperture radar (SAR) Images by integrating watershed transformation and controllable gradient vector flow (GVF) snake model[J]. IEEE Journal of Oceanic Engineering, 2012, 37(3): 375-383. doi: 10.1109/JOE. 2012.2191998. 顏學(xué)穎, 焦李成, 王凌霞, 等. 一種提高SAR 圖像分割性能的新方法[J]. 電子與信息學(xué)報(bào), 2011, 33(7): 1700-1705. doi: 10.3724/SP.J.1146.2010.01190. YAN Xueying, JIAO Licheng, WANG Lingxia, et al. New method for improving the performance of SAR image segmentation[J]. Journal of Electronics Information Technology, 2011, 33(7): 1700-1705. doi: 10.3724/SP.J.1146. 2010.01190. CASELLES V, KIMMEL R, and SAPIRO G. Geodesic active contours[J]. International Journal of Computer Vision, 1997, 22(1): 61-79. doi: 10.1023/A:1007979827043. ADALSTEINSSON D and SETHIAN J A. A fast level set method for propagating interfaces[J]. Journal of Computational Physics, 1995, 118(2): 269-277. doi: 10.1006/ jcph.1995.1098. ZHANG K, ZHANG L, SONG H, et al. Active contours with selective local or global segmentation: A new formulation and level set method[J]. Image Vision Computing, 2010, 28(4): 668-676. doi: 10.1016/j.imavis.2009.10.009. XU C, YEZZI A, and PRINCE J L. On the relationship between parametric and geometric active contours[C]. IEEE Signals, Systems and Computers, Asilomar, USA, 2000: 483-489. doi: 10.1109/ACSSC.2000.911003. LIU Z L, LI N, WANG R, et al. A novel region-merging approach for coastline extraction from Sentinel-1A IW mode SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 324-328. doi: 10.1109/LGRS.2015. 2510745. BASELICE F and FERRAIOLI G. Unsupervised coastal line extraction from SAR images[J]. IEEE Geoscience Remote Sensing Letters, 2013, 10(6): 1350-1354. doi: 10.1109/LGRS. 2013.2241013. SHU Y M, LI J, and YOUSIF H. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring[J]. Remote Sensing of Environment, 2010, 114(9): 2026-2035. doi: 10.1016/j.rse.2010.04.009. -
計(jì)量
- 文章訪問數(shù): 1291
- HTML全文瀏覽量: 224
- PDF下載量: 367
- 被引次數(shù): 0