SAR圖像的自動分割方法研究
Automatic Segmentation for Synthetic Aperture Radar Images
-
摘要: 由于存在相干斑噪聲的影響，給SAR圖像分割造成很大的困難，該文提出了一種SAR圖像的自動分割方法。首先在特征提取階段，通過計算小波能量提取紋理信息，用鄰域統(tǒng)計量提取灰度信息，用保邊緣平均灰度提取邊緣信息，以確保邊緣準(zhǔn)確。然后提出一種改進(jìn)的完全無監(jiān)督的聚類算法進(jìn)行圖像分割，該算法可以自動確定分割的類型數(shù)目。由于該方法充分考慮了SAR圖像的紋理、灰度和邊緣信息，因而極大地提高了其最終分割性能。實驗結(jié)果證明了該方法的有效性。
-
關(guān)鍵詞:
- SAR圖像;特征提取;無監(jiān)督聚類;分割
Abstract: The multiplicative nature of the speckle noise in SAR images is a big problem in SAR image segmentation. A novel method for automatic segmentation of SAR images is proposed. The wavelet energy is used to extract texture features, the regional statistics is used to extract gray-level features and the edge preserving mean of gray-level features is used to ensure the accuracy of classification of pixels near to the edge. Three representative kinds of features of SAR image are extracted, so the segmentation performance is enhanced. Besides, an improved unsupervised clustering algorithm is proposed for image segmentation, which can determine the number of classes automatically. Segmentation results on real SAR image demonstrate the effectiveness of the proposed method. -
Oliver C. Understanding Synthetic Aperture Radar Image. Boston London: Arrech House, 1998：88-204.[2]侯彪, 劉芳, 焦李成. 基于小波變換的高分辨SAR港口目標(biāo)自動分割. 紅外與毫米波學(xué)報, 2002, 21(5): 385-389.[3]Daubechies I. The wavelet transform: time-frequency localization and signal analysis[J].IEEE Trans. on Information Theory.1990, 36(5):961-1005[4]Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1989, 11(7): 647-693.[5]章毓晉. 圖像分割. 北京: 科學(xué)出版社, 2001年, 第六章.[6]Wong C C，Chen C C. A hybrid clustering and gradient descent approach for fuzzy modeling. IEEE Trans. on SMC-Part B, 1999, 29(6): 686-693.[7]Dubes R C, et al.. MRF-based algorithms for image segmentation. Proceedings of 3rd ICIPIA, Warwick, UK, 1990：808-814. -
計量
- 文章訪問數(shù): 8603
- HTML全文瀏覽量: 139
- PDF下載量: 1083
- 被引次數(shù): 0
下載:
