Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

Ship classification using synthetic aperture radar (SAR) induces effective marine applications but suffers from imbalanced datasets. Common ship types (majority classes) contain many more instances than rare ship types (minority classes), resulting in performance loss since minority instances tend to be ignored. Meanwhile, it is difficult and time-consuming to produce reliable instances for rare ships. The synthetic minority oversampling techniques have shown great potential to balance the distribution of classes by synthesizing minority instances. However, the newly synthesized instances may overlap with the majority instances, reducing the separability among classes, or being far away from the classification boundary, which is meaningless. This article proposes a clustering-based size-adaptive safer oversampling (C-SASO) technique to address the imbalanced classification problem. Proven schemes, including cluster minority instances, adaptively allocate oversampling sizes, and assign weights are adopted for selecting minority instances. Then, new instances are synthesized according to the safe metric of selected instances, instead of randomly inserted. Furthermore, multiple handcrafted features are tested to provide a clearer classification boundary. The proposed comprehensively considers the usefulness and safety of synthesizing instances. Experiments on the OpenSARShip and the FUSAR-Ship datasets demonstrate that the proposed technique achieves significantly better results.

Light Detection and Ranging (LiDAR) produces 3D point clouds that describe ground objects, and has been used to make object interpretation in many cases. However, traditional LiDAR only records discrete echo signals and provides limited feature parameters of point clouds, while full-waveform LiDAR (FWL) records the backscattered echo in the form of a waveform, which provides more echo information. With the development of machine learning, support vector machine (SVM) is one of the commonly used classifiers to deal with high dimensional data via small amount of samples. Ensemble learning, which combines a set of base classifiers to determine the output result, is presented and SVM ensemble is used to improve the discrimination ability, owing to small differences in features between different types of data. In addition, previous kernel functions of SVM usually cause under-fitting or over-fitting that decreases the generalization performance. Hence, a series of kernel functions based on wavelet analysis are used to construct different wavelet SVMs (WSVMs) that improve the heterogeneity of ensemble system. Meanwhile, the parameters of SVM have a significant influence on the classification result. Therefore, in this paper, FWL point clouds are classified by WSVM ensemble and particle swarm optimization is used to find the optimal parameters of WSVM. Experimental results illustrate that the proposed method is robust and effective, and it is applicable to some practical work.

<!-- *** Custom HTML *** --> The constant false alarm rate (CFAR) detector is a classical algorithm for ship detection with synthetic aperture radar (SAR). However, the algorithm is susceptible to the accuracy of sea clutter modeling and the desired probability of false alarm, thus reducing detection performance. Therefore, a goodness-of-fit test and a certain number of ship detection experiments and theoretical analysis of false alarms have been extensively practiced as prior knowledge. Compared to earlier SAR sensors, the newly launched Sentinel-1 has nearly uniform signal-to-noise ratio, and the distributed-target-ambiguity ratio may provide additional capabilities for ship detection. Owing to the complex interaction between SAR system and sea surface, the previous work may not be completely suitable for Sentinel-1. As its application is in the beginning, further research is needed. We evaluate the effectiveness of model fitting among five commonly used distributions, and the influences of incident angle, polarization, and sea state on modeling are analyzed. In addition, CFAR detectors constructed by these distributions carried out the ship detection experiments. Moreover, the false alarms that are inevitably caused during the ship detection are classified and statistically analyzed. These aforementioned works can provide an important reference for Sentinel-1 to implement large-scale, as well as long-term, ship detection activities and for further improvement.