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An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery

机译：用于高空间分辨率遥感影像的高效，鲁棒的集成地理空间物体检测框架

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摘要

Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a significant and challenging problem when further analyzing object-related information for civil and engineering applications. However, the computational efficiency and the separate region generation and localization steps are two big obstacles for the performance improvement of the traditional convolutional neural network (CNN)-based object detection methods. Although recent object detection methods based on CNN can extract features automatically, these methods still separate the feature extraction and detection stages, resulting in high time consumption and low efficiency. As a significant influencing factor, the acquisition of a large quantity of manually annotated samples for HSR remote sensing imagery objects requires expert experience, which is expensive and unreliable. Despite the progress made in natural image object detection fields, the complex object distribution makes it difficult to directly deal with the HSR remote sensing imagery object detection task. To solve the above problems, a highly efficient and robust integrated geospatial object detection framework based on faster region-based convolutional neural network (Faster R-CNN) is proposed in this paper. The proposed method realizes the integrated procedure by sharing features between the region proposal generation stage and the object detection stage. In addition, a pre-training mechanism is utilized to improve the efficiency of the multi-class geospatial object detection by transfer learning from the natural imagery domain to the HSR remote sensing imagery domain. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset were conducted to evaluate the proposed method.
机译：在进一步分析民用和工程应用中与对象有关的信息时，从高空间分辨率（HSR）遥感影像中进行地理空间对象检测是一个重大且具有挑战性的问题。然而，计算效率以及单独的区域生成和定位步骤是传统基于卷积神经网络（CNN）的目标检测方法性能提高的两个主要障碍。尽管最近的基于CNN的物体检测方法可以自动提取特征，但是这些方法仍然将特征提取和检测阶段分开，导致高时间消耗和低效率。作为重要的影响因素，为HSR遥感影像对象采集大量手动注释的样本需要专家经验，这既昂贵又不可靠。尽管在自然图像对象检测领域取得了进展，但是复杂的对象分布使得难以直接处理高铁遥感图像对象检测任务。针对上述问题，提出了一种基于快速区域卷积神经网络（Faster R-CNN）的高效，鲁棒的综合地理空间目标检测框架。所提出的方法通过在区域提议生成阶段和对象检测阶段之间共享特征来实现集成过程。另外，通过从自然图像域到HSR遥感图像域的转移学习，利用预训练机制来提高多类地理空间物体检测的效率。对公开可用的10类物体检测数据集进行了广泛的实验和综合评估，以评估该方法。

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