当前位置: X-MOL 学术ISPRS J. Photogramm. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Region level SAR image classification using deep features and spatial constraints
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2020-03-07 , DOI: 10.1016/j.isprsjprs.2020.03.001
Anjun Zhang , Xuezhi Yang , Shuai Fang , Jiaqiu Ai

The region-level SAR image classification algorithms which combine CNN (Convolutional Neural Networks) with super-pixel have been proposed to enhance the classification accuracy compared with the pixel-level algorithms. However, the spatial constraints between the super-pixel regions are not considered, which may limit the performance of these algorithms. To address this problem, an RCC-MRF (RCC, Region Category Confidence-degree) and CNN based region-level SAR image classification algorithm which explores the deep features extracted by CNN and the spatial constraints between super-pixel regions is proposed in this paper. The initial labels of super-pixel regions are obtained using a voting strategy based on the predicted labels CNN. The unary energy function of RCC-MRF is designed to find the category that a region most probably belongs to by using the RCC term which is constructed based on the probability distributions over all categories of pixels predicted by CNN. The binary energy function of RCC-MRF explores the spatial constraints between the adjacent super-pixel regions. In our proposed algorithm, the pixel-level misclassifications can be reduced by the smoothing within regions and the region-level misclassifications will be rectified by minimizing the energy function of RCC-MRF. Experiments have been done on simulated and real SAR images to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm notably outperforms the other CNN-based region-level SAR image classification algorithms.



中文翻译:

使用深度特征和空间约束的区域级SAR图像分类

提出了将CNN(卷积神经网络)与超像素相结合的区域级SAR图像分类算法,以提高分类精度。但是,没有考虑超像素区域之间的空间约束,这可能会限制这些算法的性能。为了解决这个问题,提出了一种基于RCC-MRF(RCC,区域类别置信度)和CNN的区域级SAR图像分类算法,该算法探索了CNN提取的深层特征和超像素区域之间的空间约束。 。基于预测的标签CNN,使用投票策略获得超像素区域的初始标签。RCC-MRF的一元能量函数旨在通过使用RCC项来查找区域最可能属于的类别,该RCC项是基于CNN预测的所有像素类别的概率分布构造的。RCC-MRF的二进制能量函数探索了相邻超像素区域之间的空间约束。在我们提出的算法中,可以通过区域内的平滑来减少像素级错误分类,并且可以通过最小化RCC-MRF的能量函数来纠正区域级错误分类。已经在模拟和真实SAR图像上进行了实验,以评估所提出算法的性能。实验结果表明,该算法明显优于其他基于CNN的区域级SAR图像分类算法。

更新日期:2020-03-07
down
wechat
bug