Abstract
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.
Highlights
I N RECENT years, with the rapid development of remote sensing technology, remote sensing images’ spatial resolution has gradually increased, which makes the demand for Manuscript received September 1, 2020; revised October 21, 2020; accepted October 31, 2020
We propose a unified framework for joint superresolution and aircraft recognition network (Joint-SRARNet), which attempts to generate distinctive highresolution aircraft images from low-resolution remote sensing images to achieve better aircraft recognition performance
We propose a unified framework for joint superresolution and aircraft recognition to address aircraft recognition in low-resolution remote sensing images to solve the above problems
Summary
I N RECENT years, with the rapid development of remote sensing technology, remote sensing images’ spatial resolution has gradually increased, which makes the demand for Manuscript received September 1, 2020; revised October 21, 2020; accepted October 31, 2020. (2) Fine-grained aircraft recognition methods, such as MultiCAM [4], mine global and local discriminative features to distinguish different aircraft types Both frameworks for CNN-based aircraft recognition have achieved impressive results on clear and high-resolution remote sensing images. Their performance sharply degrades for the recognition of blurred aircraft in low-resolution remote sensing images because 1) small blurred aircraft lack detailed information, and 2) the small interclass changes and large intra-class changes result from the fine-grained characteristics, making this task extremely challenging. The purpose of superresolution technology is to make images clear and, more importantly, to make superresolution images easy to classify and recognize To address these problems, we propose a unified framework for joint superresolution and aircraft recognition network (Joint-SRARNet), which attempts to generate distinctive highresolution aircraft images from low-resolution remote sensing images to achieve better aircraft recognition performance.
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