基于知识推理和视觉机理的遥感图像目标识别方法研究
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摘要
随着遥感技术的发展,遥感成像的波谱、空间、时间分辨率不断提高,使得遥感传感器采集并传输到地面的影像数据急剧增加。如何从海量遥感影像数据中正确识别指定地物目标是一个极富挑战性而又迫切需要解决的重要课题。模拟人的视觉功能研究人识别目标的过程和人识别目标的生理机理是研究这个课题的一个重要方向。人之所以能在一幅遥感图像中快速准确地识别指定目标是因为人具有必要的知识,以及通过知识推理获得新知识的能力。这种运用知识和能力的基础是人类经过长期进化而形成的完善的视觉感知系统。因此,研究如何将知识推理和视觉机理应用于遥感图像地物目标识别具有重要的价值。
本文模仿人的知识推理能力和部分视觉机理,研究了知识推理和视觉机理在遥感图像地物目标识别中的应用。研究内容包括基于知识推理的遥感地物目标识别方法；基于视觉前馈模型的遥感纹理目标识别方法；基于周边抑制机理的遥感地物目标背景纹理抑制。
首先,本文提出了一种基于知识的遥感图像目标识别框架和遥感地物目标通用知识库设计方案。基于知识的遥感地物目标识别框架以知识为中心,通过知识推理完成地物目标识别任务,能够在目标识别过程中更好地实现知识的共享和复用。依据该框架,我们实现了基于知识推理的机场目标识别。遥感地物目标通用知识库以本体为知识表示形式,它能够更好地实现对知识的管理和运用,可以更充分发挥知识推理的作用。本文还对基于知识的目标识别框架的关键环节——语义映射方法进行了初步的研究。
其次,基于视觉前馈模型,本文使用标准模型特征实现了对遥感图像中典型纹理目标——市郊居民区目标的识别。在单独使用标准模型特征识别纹理目标时,对于目标区域边界附近的像素常常会产生误识别。我们通过把纹理区域划分结果与使用标准模型特征识别结果相结合,减小了误识别的影响,实现了对市郊居民区目标的有效识别。另外,我们通过在目标识别步骤之前进行郊区城区环境判别和纹理区域划分,预先排除了无关的信息,有利于最后提高正确识别率。实验结果表明了我们方法的有效性。
本文还将周边抑制机理运用于目标周围背景纹理的抑制,达到了有效减少目标的背景纹理,从而突出目标轮廓的目的,为完成目标识别提供了有利的条件。周边抑制机理是人视觉系统所具有的对目标轮廓周边的纹理进行抑制而保留轮廓本身的视觉机理。本文把周边抑制机理同图像多尺度特性相结合,通过周边抑制机理对相互靠近的纹理边缘的强抑制作用和多尺度特性对轮廓的保留作用,显著地抑制了背景纹理,获得良好的轮廓保留效果,为正确识别目标减少了计算量,有利于提高正确识别率。对机场目标的实验结果表明了本文方法的实用性。
最后,给出了本文总结和下一步工作的展望。
With the development of remote sensing technologies, spectral, spatial and temporal resolutions in remote sensing continually elevate, and thus the amounts of image data, which are acquired by remote sensor and then transferred to ground, drastically increase. How to properly recognize specific ground targets from huge-amount remote sensing image data becomes a quite challenging and urgently demanding important research topic. By simulating human visual functionality, studying the recognition process and physiological mechanisms of human to recognize targets is a critical research direction. The reason for human's capability of rapidly and properly recognizing specific target is that human have necessary knowledge and can acquire new knowledge via knowledge inference. The basis of the ability of employing knowledge is the perfect visual perceptual system which is formed via long-period evolution. Therefore, researches on how to apply knowledge inference and visual mechanisms to recognition of ground targets in remote sensing images are important.
In this dissertation, the human's ability of knowledge inference and partial visual mechanisms of human are simulated, and the application of knowledge inference and visual mechanisms to ground target recognition in remote sensing images is researched. Research content include remote sensing ground targets recognition method based on knowledge inference, remote sensing textural target recognition method based on visual feed-forward model, background texture suppression for remote sensing targets based on surround suppression mechanism.
First, for target recognition in remote sensing images, a knowledge-based framework and an architecture design scheme of a general knowledge base were proposed. The knowledge-based target recognition framework centers on knowledge and can better achieve sharing and re-using of knowledge during target recognition process. The framework accomplishes target recognition tasks mainly through knowledge inference. According to the proposed framework, airport target recogntion based on knowledge inference was obtained. The general knowledge base take ontology as knowledge representation form, and it can better achieve managing and employing knowledge, and also it can make knowledge inference be a more important role. Besides, in this dissertation, primary researches on the key step in the target recognition framework, i.e., semantic mapping method, was presented.
Secondly, based on visual feed-forward model and using Standard Model Features (SMF), recogniton for the typical textural target, suburb inhabitant, in remote sensing image is addressd. When recognizing textural targets using SMF alone, the pixels near target region boundary are often mis-recognized. The influence of mis-recognition is reduced via combination of textural region partition results and SMF recognition results, and thus effective recognition for suburb inhabitant target is achieved. In addition, irrelevant information was excluded in advance via circumstance discrimination between suburb regions and urban regions, textural region partitions, which favor the elevation of final proper recognition. Experiment results demonstrate the effectiveness of our method.
Thirdly, surround suppression mechanism was applied to the suppression of background textures surrounding a target, and the goal of effectively reducing background textures and giving prominence to target contour is achieved, which provides beneficial condition for target recognition. Surround supperession mechanism is the visual mechanism of human visual system, which can suppress textures surrounding target contour and maintain contour itself. We combine surround suppression mechanism with multiscale properties of images, and through strong suppression to textural edges as well as maintenance of contour, background textures are remarkably suppressed and nice contour maintenance is acquired, therefore, the computational amount for properly recognizing target reduces, which greatly increased recognition rate. Experiment results of airport target demonstrated the practicality of the presented method.
Finally, whole conclusions are made and further research directions are addressed.
本文模仿人的知识推理能力和部分视觉机理,研究了知识推理和视觉机理在遥感图像地物目标识别中的应用。研究内容包括基于知识推理的遥感地物目标识别方法；基于视觉前馈模型的遥感纹理目标识别方法；基于周边抑制机理的遥感地物目标背景纹理抑制。
首先,本文提出了一种基于知识的遥感图像目标识别框架和遥感地物目标通用知识库设计方案。基于知识的遥感地物目标识别框架以知识为中心,通过知识推理完成地物目标识别任务,能够在目标识别过程中更好地实现知识的共享和复用。依据该框架,我们实现了基于知识推理的机场目标识别。遥感地物目标通用知识库以本体为知识表示形式,它能够更好地实现对知识的管理和运用,可以更充分发挥知识推理的作用。本文还对基于知识的目标识别框架的关键环节——语义映射方法进行了初步的研究。
其次,基于视觉前馈模型,本文使用标准模型特征实现了对遥感图像中典型纹理目标——市郊居民区目标的识别。在单独使用标准模型特征识别纹理目标时,对于目标区域边界附近的像素常常会产生误识别。我们通过把纹理区域划分结果与使用标准模型特征识别结果相结合,减小了误识别的影响,实现了对市郊居民区目标的有效识别。另外,我们通过在目标识别步骤之前进行郊区城区环境判别和纹理区域划分,预先排除了无关的信息,有利于最后提高正确识别率。实验结果表明了我们方法的有效性。
本文还将周边抑制机理运用于目标周围背景纹理的抑制,达到了有效减少目标的背景纹理,从而突出目标轮廓的目的,为完成目标识别提供了有利的条件。周边抑制机理是人视觉系统所具有的对目标轮廓周边的纹理进行抑制而保留轮廓本身的视觉机理。本文把周边抑制机理同图像多尺度特性相结合,通过周边抑制机理对相互靠近的纹理边缘的强抑制作用和多尺度特性对轮廓的保留作用,显著地抑制了背景纹理,获得良好的轮廓保留效果,为正确识别目标减少了计算量,有利于提高正确识别率。对机场目标的实验结果表明了本文方法的实用性。
最后,给出了本文总结和下一步工作的展望。
With the development of remote sensing technologies, spectral, spatial and temporal resolutions in remote sensing continually elevate, and thus the amounts of image data, which are acquired by remote sensor and then transferred to ground, drastically increase. How to properly recognize specific ground targets from huge-amount remote sensing image data becomes a quite challenging and urgently demanding important research topic. By simulating human visual functionality, studying the recognition process and physiological mechanisms of human to recognize targets is a critical research direction. The reason for human's capability of rapidly and properly recognizing specific target is that human have necessary knowledge and can acquire new knowledge via knowledge inference. The basis of the ability of employing knowledge is the perfect visual perceptual system which is formed via long-period evolution. Therefore, researches on how to apply knowledge inference and visual mechanisms to recognition of ground targets in remote sensing images are important.
In this dissertation, the human's ability of knowledge inference and partial visual mechanisms of human are simulated, and the application of knowledge inference and visual mechanisms to ground target recognition in remote sensing images is researched. Research content include remote sensing ground targets recognition method based on knowledge inference, remote sensing textural target recognition method based on visual feed-forward model, background texture suppression for remote sensing targets based on surround suppression mechanism.
First, for target recognition in remote sensing images, a knowledge-based framework and an architecture design scheme of a general knowledge base were proposed. The knowledge-based target recognition framework centers on knowledge and can better achieve sharing and re-using of knowledge during target recognition process. The framework accomplishes target recognition tasks mainly through knowledge inference. According to the proposed framework, airport target recogntion based on knowledge inference was obtained. The general knowledge base take ontology as knowledge representation form, and it can better achieve managing and employing knowledge, and also it can make knowledge inference be a more important role. Besides, in this dissertation, primary researches on the key step in the target recognition framework, i.e., semantic mapping method, was presented.
Secondly, based on visual feed-forward model and using Standard Model Features (SMF), recogniton for the typical textural target, suburb inhabitant, in remote sensing image is addressd. When recognizing textural targets using SMF alone, the pixels near target region boundary are often mis-recognized. The influence of mis-recognition is reduced via combination of textural region partition results and SMF recognition results, and thus effective recognition for suburb inhabitant target is achieved. In addition, irrelevant information was excluded in advance via circumstance discrimination between suburb regions and urban regions, textural region partitions, which favor the elevation of final proper recognition. Experiment results demonstrate the effectiveness of our method.
Thirdly, surround suppression mechanism was applied to the suppression of background textures surrounding a target, and the goal of effectively reducing background textures and giving prominence to target contour is achieved, which provides beneficial condition for target recognition. Surround supperession mechanism is the visual mechanism of human visual system, which can suppress textures surrounding target contour and maintain contour itself. We combine surround suppression mechanism with multiscale properties of images, and through strong suppression to textural edges as well as maintenance of contour, background textures are remarkably suppressed and nice contour maintenance is acquired, therefore, the computational amount for properly recognizing target reduces, which greatly increased recognition rate. Experiment results of airport target demonstrated the practicality of the presented method.
Finally, whole conclusions are made and further research directions are addressed.
引文
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