The classification of high-resolution (HR) synthetic aperture radar (SAR) image is of great significance to SAR scene interpretation and understanding. However, the HR SAR image contains rich ground information and complex spatial structural features. The extraction of effective distinguishing features under the influence of coherent speckle is still a challenging task. A multi-scale anisotropic convolution sparse coding (MACSC) algorithm is proposed for HR SAR image classification. First, the low-order statistical features are extracted to improve the richness of SAR image. Then an unsupervised MACSC model is utilized to learn a set of sparse feature maps and convolution filters for the above statistical features by solving the designed objective function. In MACSC, the multi-scale anisotropic Gaussian kernels are utilized to initialize the dictionary of MACSC. Compared with the isotropic Gaussian kernel of the original CSC, these kernels can more accurately describe the details of different directions and scales in the SAR scene. Meanwhile, the adaptive sparse control factor is introduced into the MACSC model, which can make the learned sparse feature maps suppress the speckle and capture the abundant edge and texture information of the SAR image. After that, the aggregation operation is conducted on multi-scale and multi-direction sparse feature maps to integrate the neighbor pixels and reduce the computing burden. Finally, the obtained feature vector is input into the support vector machine classifier to realize classification. Experiments on three challenging SAR data sets demonstrate that MACSC achieves better classification performance than related sparse representation methods.

中文翻译：

---

基于多尺度各向异性卷积稀疏编码的高分辨率合成孔径雷达图像分类

高分辨率（HR）合成孔径雷达（SAR）图像的分类对SAR场景的解释和理解具有重要意义。但是，HR SAR图像包含丰富的地面信息和复杂的空间结构特征。在相干斑点的影响下提取有效的区别特征仍然是一项艰巨的任务。提出了一种用于HR SAR图像分类的多尺度各向异性卷积稀疏编码（MACSC）算法。首先，提取低阶统计特征以提高SAR图像的丰富度。然后，通过求解设计的目标函数，利用无监督的MACSC模型为上述统计特征学习一组稀疏特征图和卷积滤波器。在MACSC中，利用多尺度各向异性高斯核对MACSC字典进行初始化。与原始CSC的各向同性高斯核相比，这些核可以更准确地描述SAR场景中不同方向和尺度的细节。同时，将自适应稀疏控制因子引入到MACSC模型中，可以使学习到的稀疏特征图抑制斑点并捕获SAR图像的丰富边缘和纹理信息。之后，对多尺度，多方向的稀疏特征图进行聚合操作，以融合相邻像素，减轻计算负担。最后，将获得的特征向量输入到支持向量机分类器中进行分类。