A Scale-Aware Pyramid Network for Multi-Scale Object Detection in SAR Images

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Hao

et al. 2022

Accurate registration between SAR images is the basis for high-precision geometric correction of SAR images. The feature points extracted by conventional feature extraction methods is unsatisfactory, which is affected by imaging geometric characteristics and speckle noise of SAR images. This paper innovatively proposes a spaceborne SAR image feature learning framework to realize automatic sample generation and model training. It mainly includes two modules: The feature sample generation module based on the initial geometric information of spaceborne SAR. The initial RPC parameters of the spaceborne SAR are adopted to realize the initial positioning of SAR image, and a variety of feature extraction operators are used to match the overlapping areas to obtain high-precision matching points, which are employed as training samples for image-pairs; Pseudo-Siamese feature learning network SARPointNet for SAR image feature learning. The pseudo-siamese network is used to extract the feature points and descriptors of the sample image-pairs. The feature optimization process is realized through the descriptor constraints between image pairs, which promotes the network to improve the accuracy of feature extraction. The proposed method has been tested in mountain, hilly, flatland and urban scenarios, respectively. The results demonstrate that the correspondence points extracted by SARPointNet are evenly distributed, large quantities (at least 10 times that of other methods) and achieve high precision (The root mean square error is less than 1 pixels), which shows great advantages over other methods.

Section: B Image Feature Extraction Based On Deep Learningmentioning

confidence: 99%

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Hao

et al. 2022

“…Due to the diversity of moving objects and the video equipment, it is necessary to adapt the object tracking algorithm to different data sources. For example, the ordinary video [1]- [3], the unmanned aerial vehicle (UAV) video [4] [5], the thermal infrared (TIR) video [6], the synthetic aperture radar (SAR) video [7] [8] and the satellite video [9]- [21] which is an emerging type of space-based video data in recent years. The satellite video has demonstrated such advantages as wide shooting range, high resolution and the capability of continuously monitoring the target objects on land.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Tracking on Satellite Video Based on Historical Model

Chen

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

et al. 2022

Vehicle tracking on satellite videos poses a challenge for the existing object tracking algorithms due to the few features, object occlusion and similar objects appearance. To improve the performance of the object tracking algorithm, a historical modelbased tracker intended for satellite videos is proposed in this study. It updates the tracker by using the historical model of each frame in the video, which contains plenty of object information and background information, so as to improve tracking ability on fewfeature objects. Furthermore, a historical model evaluation scheme is designed to obtain reliable historical models, which ensures that the tracker is sensitive to the object in the current frame, thus avoiding the impact caused by changes in object appearance and background. Besides, to solve the drift issue of the tracker caused by object occlusion and the appearance of similar objects, an anti-drift tracker correction scheme is proposed as well. According to the comparative experiments conducted on satellite videos dataset SatSOT, our tracker produces an excellent performance. Moreover, sensitivity analysis, varying criteria comparative experiments and ablation experiments are conducted to demonstrate that the proposed schemes are effective in improving the Precision and Success Rate of the tracker.

“…); xingshi luo@bit.edu.cn (X.L.)) been widely applied in many remote sensing fields, such as object detection [3]- [5], ground classification [6], [7], environmental monitoring [8], anomaly detection [9], and so on [10]. Recently, the multi-dimensional and multi-modal HSIs have expanded the possibility of more applications, such as visual recognition and tracking [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Multiple Frame Splicing and Degradation Learning for Hyperspectral Imagery Super-Resolution

Deng

Luo

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2022

Self Cite

Hyperspectral Imagery (HSI) is an emerging remote sensing technology to discriminate different remote sensing objects. However, the HSI spatial resolution is relatively low due to the trade-off in restricted physical hardware and various imaging conditions, restricting the subsequent object detection applications. At present, the Single Hyperspectral Super Resolution (SHSR) strategy has encountered the bottleneck on more precise details extraction, and the Fusion Hyperspectral Image Super Resolution (FHSR) strategy must need extra RGB/multispectral information which is not suitable for general HSI usage. Also, both types of current strategies focus less on the multiple degradation causes of low spatial resolution. In this paper, a step forward in designing a novel framework of multiple frame splicing strategy to greatly improve the SHSR quality, and applying multiple HSI degradation models to better fit the real degradation circumstance. Specifically, the framework is an end-to-end Super Resolution (SR) network that supersedes a single up-sampling module and removes complex attention residual model due to the same size of multiple splicing low-resolution input samples with high-resolution outputs. The effective framework will alleviate the vague at higher multiples, and accelerate the training convergence. Based on this framework, multiple degradation lowresolution samples can be simultaneously combined to fit better for the blind super-resolution result. Concretely, the degradation focus on the blur, noise, compression, and their combinations to simulate the real degradation. Experimental results on three different hyperspectral datasets demonstrate that the proposed MFSDM algorithm can significantly enhance the details in the recovered high-resolution hyperspectral images, and outperforms the state-of-the-art SHSR methods.