Semantic segmentation is one of the essential prerequisites for computer vision tasks, but edge-precise segmentation stays challenging due to the potential lack of a proper model indicating the low-level relation between pixels. We have presented Refined UNet v2, a concatenation of a network backbone and a subsequent embedded conditional random field (CRF) layer, which coarsely performs pixel-wise classification and refines edges of segmentation regions in a one-stage way. However, the CRF layer of v2 employs a gray-scale global observation (image) to construct contrast-sensitive bilateral features, which is not able to achieve the desired performance on ambiguous edges. In addition, the naïve depth-wise Gaussian filter cannot always compute efficiently, especially for a longer-range message-passing step. To address the aforementioned issues, we upgrade the bilateral message-passing kernel and the efficient implementation of Gaussian filtering in the CRF layer in this paper, referred to as Refined UNet v3, which is able to effectively capture ambiguous edges and accelerate the message-passing procedure. Specifically, the inherited UNet is employed to coarsely locate cloud and shadow regions and the embedded CRF layer refines the edges of the forthcoming segmentation proposals. The multi-channel guided Gaussian filter is applied to the bilateral message-passing step, which improves detecting ambiguous edges that are hard for the gray-scale counterpart to identify, and fast Fourier transform-based (FFT-based) Gaussian filtering facilitates an efficient and potentially range-agnostic implementation. Furthermore, Refined UNet v3 is able to be extended to segmentation on multi-spectral datasets, and the corresponding refinement examination confirms the development of shadow retrieval. Experiments and corresponding results demonstrate that the proposed update can outperform its counterpart in terms of the detection of vague edges, shadow retrieval, and isolated redundant regions, and it is practically efficient in our TensorFlow implementation. The demo source code is available at https://github.com/92xianshen/refined-unet-v3.

语义分割是计算机视觉任务的必要先决条件之一，但由于可能缺乏指示像素之间低级关系的适当模型，因此边缘精确分割仍然具有挑战性。我们提出了 Refined UNet v2，这是一个网络主干和随后的嵌入式条件随机场 (CRF) 层的串联，它粗略地执行像素级分类并以一个阶段的方式细化分割区域的边缘。然而，v2的CRF层采用灰度全局观察（图像）来构建对比敏感的双边特征，在模糊边缘上无法达到预期的性能。此外，朴素的深度高斯滤波器不能总是有效地计算，尤其是对于更远距离的消息传递步骤。针对上述问题，我们在本文中升级了双边消息传递内核和高斯滤波在 CRF 层的高效实现，称为 Refined UNet v3，它能够有效地捕获模糊边缘并加速消息传递过程。具体来说，继承的 UNet 用于粗略定位云和阴影区域，嵌入的 CRF 层细化即将到来的分割建议的边缘。多通道引导高斯滤波器应用于双边消息传递步骤，改善了灰度对应物难以识别的模糊边缘的检测，基于快速傅里叶变换（基于 FFT）的高斯滤波有利于高效和潜在的范围不可知的实现。此外，Refined UNet v3 能够扩展到多光谱数据集上的分割，相应的细化检查证实了阴影检索的发展。实验和相应的结果表明，所提出的更新在检测模糊边缘、阴影检索和孤立的冗余区域方面可以优于其对应的更新，并且在我们的 TensorFlow 实现中实际上是有效的。演示源代码可在 https://github.com/92xianshen/refined-unet-v3 获得。它在我们的 TensorFlow 实现中实际上是有效的。演示源代码可在 https://github.com/92xianshen/refined-unet-v3 获得。它在我们的 TensorFlow 实现中实际上是有效的。演示源代码可在 https://github.com/92xianshen/refined-unet-v3 获得。