Liu Jiayin, Tang Zhenmin, Cui Ying, Wu Guoxing
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel). 2017 Jun 12;17(6):1364. doi: 10.3390/s17061364.
Remote sensing technologies have been widely applied in urban environments' monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the "salt and pepper" phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.
遥感技术已广泛应用于城市环境监测、综合与建模。基于超像素的方法通过整合感知连贯区域中的空间信息，能够有效消除逐像素方法中常见的“椒盐”现象。与固定大小的窗口相比，超像素对于不同的空间结构具有自适应的大小和形状。此外，由于图像基元数量大幅减少，基于超像素的算法可显著提高计算效率。因此，超像素算法作为一种预处理技术，在遥感及许多其他领域越来越受到广泛应用。在本文中，我们提出了一种名为局部竞争超像素分割（SSLC）的超像素分割算法，该算法利用局部竞争机制来构建能量项并标记像素。局部竞争机制使得能量项具有局部性和相对性，因此，所提出的算法对图像内容和场景布局的多样性不太敏感。结果，SSLC在不同图像区域能够实现一致的性能。此外，引入了通过高斯核的核密度估计（KDE）来估计的概率密度函数（PDF），以作为一种更精细和准确的度量来描述超像素的颜色分布。为了降低计算复杂度，引入了一种边界优化框架，仅处理边界像素而非整个图像。我们进行实验，在伯克利分割数据集（BSD）和遥感图像上，将所提出的算法与其他现有最先进算法进行基准测试。结果表明，SSLC算法产生了最佳的整体性能，同时计算时间效率仍然具有竞争力。
