Xin Gao

Bio: Xin Gao is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 14, co-authored 54 publications receiving 545 citations. Previous affiliations of Xin Gao include Nanjing University of Aeronautics and Astronautics & University of Arizona.

Semantic Segmentation of Aerial Images With Shuffling Convolutional Neural Networks

Abstract: Semantic segmentation of aerial images refers to assigning one land cover category to each pixel. This is a challenging task due to the great differences in the appearances of ground objects. Many attempts have been made during the past decades. In recent years, convolutional neural networks (CNNs) have been introduced in the remote sensing field, and various solutions have been proposed to realize dense semantic labeling with CNNs. In this letter, we propose shuffling CNNs to realize semantic segmentation of aerial images in a periodic shuffling manner. This approach is a supplement to current methods for semantic segmentation of aerial images. We propose a naive version and a deeper version of this method, and both are adept at detecting small objects. Additionally, we propose a method called field-of-view (FoV) enhancement that can enhance the predictions. This method can be applied to various networks, and our experiments verify its effectiveness. The final results are further improved through an ensemble method that averages the score maps generated by the models at different checkpoints of the same network. We evaluate our models using the ISPRS Vaihingen and Potsdam data sets, and we acquire promising results using these two data sets.

On the MUSIC-derived Approaches of Angle Estimation for Bistatic MIMO Radar

Abstract: We investigate the topic for the direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic multiple-input-multiple-output (MIMO) radar systems with the exploitation of array invariance. Several MUSIC-derived algorithms for angle estimation in MIMO radar have been presented and compared for their complexity costs against that of ESPRIT. The proposed scheme of multi-invariance multiple signal classification (MI-MUSIC) has the best performance and also can be considered as a generalization of MUSIC. Simulations verify the collaborative usefulness of our algorithm.

Double Similarity Distillation for Semantic Image Segmentation

Abstract: The balance between high accuracy and high speed has always been a challenging task in semantic image segmentation. Compact segmentation networks are more widely used in the case of limited resources, while their performances are constrained. In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively. Specifically, we propose a pixel-wise similarity distillation (PSD) module that utilizes residual attention maps to capture more detailed spatial dependencies across multiple layers. Compared with exiting methods, the PSD module greatly reduces the amount of calculation and is easy to expand. Furthermore, considering the differences in characteristics between semantic segmentation task and other computer vision tasks, we propose a category-wise similarity distillation (CSD) module, which can help the compact segmentation network strengthen the global category correlation by constructing the correlation matrix. Combining these two modules, DSD framework has no extra parameters and only a minimal increase in FLOPs. Extensive experiments on four challenging datasets, including Cityscapes, CamVid, ADE20K, and Pascal VOC 2012, show that DSD outperforms current state-of-the-art methods, proving its effectiveness and generality. The code and models will be publicly available.

The Gray Analysis and Machine Learning for Device-Free Multitarget Localization in Passive UHF RFID Environments

Abstract: The device-free localization (DFL) has promising application prospects in intrusion detection, emergency rescue, and smart homes, because it does not require the target to carry any auxiliary positioning equipment. Radio tomographic imaging (RTI) is one of the most potential DFL techniques and has many advantages over other methods. However, in passive ultrahigh frequency radio frequency identification scenario, there are few researches and many problems to be solved. The difficult but urgent matter is how to identify the locations of multiple targets from many false targets and artifacts. This paper proposes a novel method based on cross-sectional scan (CSS), gray value distribution analysis (GVDA), and naive Bayes classifier to solve this problem. The CSS obtains the gray value distributions of the local maximum pixel in an RTI reconstructed image. Then, the GVDA extracts several characteristic parameters from gray value distributions, such as the size, height, and shape of the peak. Finally, the naive Bayes classifier utilizes these series of characteristics to judge whether local maximum pixels are false targets or real targets. The method can also recognize the number of targets that are very close to each other. Simulation and experimental results show that this method can accurately determine the locations and the number of targets.

Novel blind carrier frequency offset estimation for OFDM system with multiple antennas

Abstract: In this paper, we address the problem of carrier frequency offset (CFO) estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems with multiple antennas. The received signal can be denoted as a trilinear model, then the trilinear decomposition-based CFO estimation algorithm is proposed. Comparing to both ESPRIT method and the cyclostationarity (CS) approach, the algorithm that we presented has improved CFO estimation performance. Furthermore, our proposed algorithm can even work in condition of no virtual carrier. Simulation results illustrate performance of this algorithm.

Deep learning in remote sensing applications: A meta-analysis and review

Abstract: Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

Overview of Multicarrier CDMA

Abstract: This chapter contains sections titled: Introduction Overview of Multicarrier CDMA Systems Channel Model Performance of MC-CDMA System Performance of Overlapping Multicarrier DS-CDMA Systems Performance of Multicarrier DS-CDMA-I Systems Performance of AMC DS-CDMA Systems Performance of SFH/MC DS-CDMA Systems Chapter Summary and Conclusion ]]>

Design study for a CDMA-based third-generation mobile radio system

Abstract: This paper focuses on a CDMA design study for future third-generation mobile and personal communication systems such as FPLMTS and UMTS. In the design study, a rigorous top down approach is adopted starting from the most essential objectives and requirements of universal third-generation mobile systems. Emphasis is laid on high flexibility with respect to the implementation of a wide range of services and service bit rates including variable rate and packet services. Flexibility in frequency and radio resource management, system and service deployment, and easy operation in mixed-cell and multioperator scenarios are further important design goals. The system concept under investigation is centered around an open and flexible radio interface architecture based on asynchronous direct-sequence CDMA with three different chip rates of approximately 1,5, and 20 Mchip/s