遥感影像弱小目标智能解译算法研究

Research on Intelligent Interpretation Algorithms for Weak and Small Targets in Remote Sensing Images

摘要:

随着遥感技术的快速发展，光学遥感影像弱小目标智能解译成为遥感信息处理的研究热点之一。遥感影像的地物目标常具有尺度小、种类多、数量大、部分重点小目标移动速度快的特点，易受到复杂背景环境及噪声影响，使得提取遥感影像弱小目标的信息面临着巨大的挑战。早期智能解译算法中的弱小目标分割、检测及跟踪等算法研究，多依赖模板匹配及先验知识，此类算法需耗费大量资源、算力及专家知识成本，存在着计算量大、泛化能力差的问题。近年来，随着深度学习等人工智能技术的快速发展，在海量遥感数据中准确获取弱小目标的信息，通过结合深度学习算法可对弱小目标的特征进行快速提取，以提供高效、准确的解译信息。本文综述了遥感影像弱小目标智能解译算法研究进展，包括基于传统图像处理方法的弱小目标分割、检测和跟踪算法，以及基于深度学习等典型相关算法。通过分析这些方法的优点与局限性，对于提高相关目标的信息获取能力、提升观测的态势感知水平以及未来应用等方面具有重要意义。

Abstract:

With the rapid development of remote sensing technology, the intelligent decoding of weak targets in optical remote sensing images has become one of the research hotspots in remote sensing information processing. The feature targets of remote sensing images are often characterized by small scale, many types, a large number, fast moving speed of some key small targets, and are easily affected by the complex background environment and noise, which makes it a great challenge extract information from weak targets in remote sensing images. Early research on weak target segmentation, detection, and tracking algorithms in intelligent interpretation algorithms mostly relied on template matching and a priori knowledge, and such algorithms need to consume a lot of resources, arithmetic, and expert knowledge costs, and there were problems of large computational volume and poor generalization ability. In recent years, with the rapid development of deep learning and other artificial intelligence technologies, the information of weak targets can be accurately obtained in massive remote sensing data, and the features of weak targets can be quickly extracted by combining deep learning algorithms to provide efficient and accurate decoding information. This paper summarizes the research progress of intelligent interpretation algorithms for weak targets in remote sensing images, including weak target segmentation, detection, and tracking algorithms based on traditional image processing methods, as well as typical related algorithms based on deep learning. By analyzing the advantages and limitations of these methods, it is of great significance to improve the information acquisition ability of relevant targets, enhance the situational awareness level of observation, and future applications.