Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field.

Infrared and visible image fusion can obtain combined images with salient hidden objectives and abundant visible details simultaneously. In this paper, we propose a novel method for infrared and visible image fusion with a deep learning framework based on a generative adversarial network (GAN) and a residual network (ResNet). The fusion is accomplished with an adversarial game and directed by the unique loss functions. The generator with residual blocks and skip connections can extract deep features of source image pairs and generate an elementary fused image with infrared thermal radiation information and visible texture information, and more details in visible images are added to the final images through the discriminator. It is unnecessary to design the activity level measurements and fusion rules manually, which are now implemented automatically. Also, there are no complicated multi-scale transforms in this method, so the computational cost and complexity can be reduced. Experiment results demonstrate that the proposed method eventually gets desirable images, achieving better performance in objective assessment and visual quality compared with nine representative infrared and visible image fusion methods.

Image super-resolution (SR) technique can improve the spatial resolution of images without upgrading the imaging system. As a result, SR promotes the development of high resolution (HR) remote sensing image applications. Many remote sensing image SR algorithms based on deep learning have been proposed recently, which can effectively improve the spatial resolution under the constraints of HR images. However, images acquired by remote sensing imaging devices typically have lower resolution. Hence, an insufficient number of HR remote sensing images are available for training deep neural networks. In view of this problem, we propose an unsupervised SR method that does not require HR remote sensing images. The proposed method introduces a generative adversarial network (GAN) that obtains SR images through the generator; then, the SR images are downsampled to train the discriminator with low resolution (LR) images. Our method outperformed several methods in terms of the quality of the obtained SR images as measured by 6 evaluation metrics, which proves the satisfactory performance of the proposed unsupervised method for improving the spatial resolution of remote sensing images.

The complementary characteristics of SAR and optical images are beneficial in improving the accuracy of land cover classification. Deep learning-based models have achieved some notable results. However, how to effectively extract and fuse the unique features of multi-modal images for pixel-level classification remains challenging. In this article, a two-branch supervised semantic segmentation framework without any pretrained backbone is proposed. Specifically, a novel symmetric attention module is designed with improved strip pooling. The multiple long receptive fields can better perceive irregular objects and obtain more anisotropic contextual information. Meanwhile, to solve the semantic absence and inconsistency of different modalities, we construct a multi-scale fusion module, which is composed of atrous spatial pyramid pooling, varisized convolutions and skip connections. A joint loss function is introduced to constrain the backpropagation and reduce the impact of class imbalance. Validation experiments were implemented on the DFC2020 and WHU-OPT-SAR datasets. The proposed model achieved the best quantitative values on the metrics of OA, Kappa and mIoU, and its class accuracy was also excellent. It is worth mentioning that the number of parameters and the computational complexity of the method are relatively low. The adaptability of the model was verified on RGB–thermal segmentation task.