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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >CRIM-FCHO: SAR Image Two-Stage Segmentation With Multifeature Ensemble
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CRIM-FCHO: SAR Image Two-Stage Segmentation With Multifeature Ensemble

机译：CRIM-FCHO：具有多功能特征的SAR图像两阶段分割

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摘要

This paper investigates the synthetic aperture radar (SAR) image segmentation in terms of feature analysis and fusion and develops a new algorithm based on multifeature ensemble accordingly. This paper is characterized by two aspects. First, multiple heterogeneous features are extracted to accurately describe the objects in SAR images. These features are then integrated in the feature level and the similarity level, respectively, to avoid the mutual influences between different kinds of features and maximize the discriminability of the similarity measure between objects. Second, a two-stage algorithm consisting of a coarse merging stage and a fine classification stage is proposed. In the coarse merging stage, a context-based region iterative merging algorithm is designed to merge most of the unambiguous superpixels in image domain at a high speed. In the fine classification stage, a fuzzy clustering algorithm incorporating hybrid optimization is developed to balance the efficiency and the robustness of the algorithm by simultaneously searching heuristically in the complete high-dimension feature space and searching along the direction of the gradient steepest descent in each feature subspace. The effectiveness of the proposed method has been successfully validated on synthetic and real SAR images.
机译：本文从特征分析和融合的角度研究了合成孔径雷达（SAR）图像的分割，并提出了一种基于多特征集合的新算法。本文的特点有两个方面。首先，提取多个异构特征以准确描述SAR图像中的对象。然后将这些特征分别集成到特征级别和相似级别中，以避免不同种类的特征之间的相互影响，并使对象之间相似性度量的可分辨性最大化。其次，提出了由粗合并阶段和细分类阶段组成的两阶段算法。在粗合并阶段，设计了基于上下文的区域迭代合并算法，以高速合并图像域中的大多数明确超像素。在精细分类阶段，通过在完整的高维特征空间中同时进行启发式搜索并沿每个特征中的梯度最速下降方向进行搜索，开发了一种结合了混合优化的模糊聚类算法来平衡算法的效率和鲁棒性子空间。该方法的有效性已在合成和真实SAR图像上得到了成功验证。

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