Remote sensing mainly focuses on information extraction from data acquired by sensors on satellite and aerial platforms. Here, one such area of interest is ground object detection and shape extraction. Recently launched satellites and conventional aerial platforms (such as commercial UAV and professional drones) have sensors leading to more detailed and rich data source for this purpose. From these, data most of the times come in the form of optical images and LiDAR measurements. Resolution of this acquired data has increased significantly such that most ground objects (as buildings, trees, ships, cars, airplanes) can be detected and analyzed in detail. Therefore, computer vision methods have become extremely useful in remote sensing applications such as building detection and shape extraction for urban planning; tree crown measurement for crop yield forecasting; ship detection for monitoring unlawful fishery; car detection for traffic flow monitoring and intelligent transportation; and airplane detection for military and commercial operations. Researchers proposed several methods to automate the mentioned applications since manually handling them is extremely hard and prohibitively time consuming. Unfortunately, the proposed methods focus on one object type most of the times. Therefore, there is no general method to handle all the mentioned applications using computer vision tools. To overcome this problem, we propose a general framework for object detection and shape extraction in remote sensing data. Our method is based on probabilistic representation inspired by our previous work and perceptual organization principles. Due to space limitations, we only focus on buildings, trees, ships, airplanes, and cars as objects of interest in this study. We test the proposed method on several optical images acquired by different satellites and LiDAR data obtained from an aerial platform. For all objects of interest, we provide test results on both object detection and shape extraction steps. We analyze the proposed method based on these tests and discuss its strengths and weaknesses. We also comment on possible future extensions of the proposed method.

遥感主要侧重于从卫星和空中平台的传感器获取的数据中提取信息。在此，关注的领域之一是地面物体检测和形状提取。最近发射的卫星和常规空中平台（例如商用UAV和专业无人机）都装有传感器，可以为此目的提供更详细，更丰富的数据源。从这些数据中，大多数时间以光学图像和LiDAR测量的形式出现数据。此采集数据的分辨率已大大提高，因此可以详细检测和分析大多数地面物体（例如建筑物，树木，轮船，汽车，飞机）。因此，计算机视觉方法已经在遥感应用中变得极为有用，例如建筑物检测和城市规划中的形状提取。树冠测量以预测作物产量；侦查船舶以监测非法渔业；车辆检测，用于交通流监控和智能交通；以及用于军事和商业行动的飞机检测。研究人员提出了几种使提到的应用程序自动化的方法，因为手动处理它们非常困难并且非常耗时。不幸的是，所提出的方法大多数时候只关注一种对象类型。因此，没有使用计算机视觉工具来处理所有提到的应用程序的通用方法。为了克服这个问题，我们提出了一种用于遥感数据中物体检测和形状提取的通用框架。我们的方法基于受先前工作和感性组织原则启发的概率表示。由于篇幅所限，在本研究中，我们仅关注建筑物，树木，轮船，飞机和汽车作为感兴趣的对象。我们在由不同卫星获取的几个光学图像以及从空中平台获取的LiDAR数据上测试了该方法。对于所有感兴趣的物体，我们都提供了物体检测和形状提取步骤的测试结果。我们根据这些测试分析提出的方法，并讨论其优缺点。我们还评论了该方法可能的将来扩展。我们根据这些测试分析提出的方法，并讨论其优缺点。我们还评论了该方法可能的将来扩展。我们根据这些测试分析提出的方法，并讨论其优缺点。我们还评论了该方法可能的将来扩展。