Aircraft recognition in high-resolution remote sensing images has rapidly progressed with the advance of convolutional neural networks (CNNs). However, the previous CNN-based methods may not work well for recognizing aircraft in low-resolution remote sensing images because the blurred aircraft in these images offer insufficient details to distinguish them from similar types of targets. An intuitive solution is to introduce superresolution preprocessing. However, conventional superresolution methods mainly focus on reconstructing natural images with detailed texture rather than constructing a high-resolution object with strong discriminative information for the recognition task. To address these problems, we propose a unified framework for joint superresolution and aircraft recognition (Joint-SRARNet) that tries to improve the recognition performance by generating discriminative, high-resolution aircraft from low-resolution remote sensing images. Technically, this network integrates superresolution and recognition tasks into the generative adversarial network (GAN) framework through a joint loss function. The generator is constructed as a joint superresolution and refining subnetwork that can upsample small blurred images into high-resolution ones and restore high-frequency information. In the discriminator, we introduce a new classification loss function that forces the discriminator to distinguish between real and fake images while recognizing the type of aircraft. In addition, the classification loss function is back-propagated to the generator to obtain high-resolution images with discriminative information for easier recognition. Extensive experiments on the challenging multitype aircraft of remote sensing images (MTARSI) dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a small blurred image and significant improvement in the recognition performance. To our knowledge, this is the first work on joint superresolution and aircraft recognition tasks.

中文翻译：

---

SRARNet：联合超分辨率和飞机识别的统一框架

随着卷积神经网络 (CNN) 的进步，高分辨率遥感图像中的飞机识别发展迅速。然而，之前基于 CNN 的方法可能无法很好地识别低分辨率遥感图像中的飞机，因为这些图像中模糊的飞机提供的细节不足以将它们与类似类型的目标区分开来。一个直观的解决方案是引入超分辨率预处理。然而，传统的超分辨率方法主要侧重于重建具有详细纹理的自然图像，而不是为识别任务构建具有强判别信息的高分辨率对象。为了解决这些问题，我们提出了一个联合超分辨率和飞机识别（Joint-SRARNet）的统一框架，该框架试图通过从低分辨率遥感图像中生成有辨别力的高分辨率飞机来提高识别性能。从技术上讲，该网络通过联合损失函数将超分辨率和识别任务集成到生成对抗网络 (GAN) 框架中。生成器被构建为一个联合超分辨率和细化子网络，可以将小的模糊图像上采样为高分辨率图像并恢复高频信息。在鉴别器中，我们引入了一个新的分类损失函数，强制鉴别器在识别飞机类型的同时区分真假图像。此外，分类损失函数被反向传播到生成器以获得具有判别信息的高分辨率图像，以便于识别。对具有挑战性的多类型飞机遥感图像 (MTARSI) 数据集进行的大量实验证明了所提出的方法在从模糊的小图像中恢复清晰的超分辨率图像方面的有效性，并显着提高了识别性能。据我们所知，这是关于联合超分辨率和飞机识别任务的第一项工作。对具有挑战性的多类型飞机遥感图像 (MTARSI) 数据集进行的大量实验证明了所提出的方法在从模糊的小图像中恢复清晰的超分辨率图像方面的有效性，并显着提高了识别性能。据我们所知，这是关于联合超分辨率和飞机识别任务的第一项工作。对具有挑战性的多类型飞机遥感图像 (MTARSI) 数据集进行的大量实验证明了所提出的方法在从模糊的小图像中恢复清晰的超分辨率图像方面的有效性，并显着提高了识别性能。据我们所知，这是关于联合超分辨率和飞机识别任务的第一项工作。