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1、 选择性视觉注意论文：基于选择性视觉注意机制的遥感图选择性视觉注意论文：基于选择性视觉注意机制的遥感图像舰船目标检测与识别像舰船目标检测与识别【中文摘要】近年来,遥感舰船检测与识别问题因其在海洋渔业、海上运输管制和海上军事等领域的重要应用越来越受到人们关注。传统方法往往需要对所有图像区域进行验证,但实际上所关心的内容通常仅占图像中很小一部分,这种全面的加工既造成了计算浪费,又加重了分析难度。然而,人类在面对复杂场景时,能迅速将注意力聚焦在显著目标上,并对这些目标进行优先处理,这里面存在一个视觉选择性注意的机制。本文旨在将该机制引入到遥感图像目标检测中,构建一个基于选择性视觉注意机制的遥感舰船目
2、标检测与识别系统,其中包括多光谱遥感数据的舰船检测以及单波段遥感舰船检测与识别。此外,本文并对现有的基于频域的 PQFT 模型进行了一定的拓展,使其能应用到通道数大于四的多光谱图像的显著性检测。论文的主要创新包含以下几个方面：1.将选择性视觉注意机制引入到多光谱遥感图像目标检测中,针对现有视觉注意计算模型不适于处理维度大于四的多维图像的不足,提出一种基于双四元数的视觉注意计算模型。将多维数据构建成双四元数的形式,利用其傅里叶变换的相位谱来提取显著性区域以用于显著目标检测,实现了多维数据的整体处理,并充分利用了频率域和空间域的信息。与传统的多光谱图像目标检测方法相比,该模型计算复杂度低,对各种参
3、数设置的依赖性小。2.将基于傅里叶变换相位谱信息的频域视觉注意计算模型应用到单波段高分辨率遥感图像的海上舰船目标检测与识别任务中,实现了复杂背景下的海上目标检测；将形状特征和纹理特征作为识别的特征依据,结合多层分类回归树,实现了检测后的舰船识别确认过程。从而一定程度上完成了 Bottom-up 和 Top-down 两种机制的融合使用。3.在完成舰船目标与非舰船目标之间的识别后,利用两维主成分分析提取候选舰船目标的较精细的纹理特征,再结合多层分类回归树实现不同类别舰船之间的分类识别。实验证明两维主成分分析能较好地提取出遥感舰船的纹理信息,识别效果良好,且具有较快的运算速度。【英文摘要】Rece
4、ntly, ship detection and recognition in remote sensing imagery is becoming an intriguing subject for more and more researches because of its potential applications in areas such as fishery management, vessel traffic services, and naval warfare. The traditional detectors need to check all regions of
5、the image carefully, but in fact the targets interest us only take up a small part of the whole image. This thorough processing demands high computational cost and increases the difficulty of target detection. However, the human vision can rapidly focus on conspicuous objects in clustered environmen
6、ts and selects salient visual information for further processing because of the existence of visual attention mechanism. This paper is primarily concerned with how to develop a visual attention-based system for ship detection and recognition in remote sensing imagery. The proposed attention-based sy
7、stem can be applied to ship detection in multispectral imagery, and also ship detection and recognition in single band optical images with high resolution. Moreover, we extend the existing frequency-based PQFT model so that it can be applied to saliency detection in multispectral imagery. The main i
8、nnovations of this paper can be described into three aspects as follows:1. We introduce the selective visual attention mechanism into target detection in multispectral imagery. Since the existing computational models of visual attention are not suitable to process multi-dimensional data with its dim
9、ension more than four, we propose an approach for visual attention based on biquaternion. The proposed approach describes high-dimensional data in the form of biquaternion and utilizes its phase spectrum of biquaternion Fourier transform (PBFT) to generate the required saliency map that can be used
10、for salient target detection. In our method, the multi-dimensional data can be processed as a whole, and features both in spatial and frequency domain can be extracted effectively. Compared with the traditional multispectral target detection method, our method has very low computational complexity a
11、nd does not rely on parameter settings.2. The PFT model, which is based on phase spectrum of Fourier transform, is applied for saliency detection in single band optical images with high resolution. Using this attention model, we can detect ship candidates accurately under complex background. Combing
12、 the hierarchical discriminant regression tree (HDR Tree), a supervised classification approach based on shape and texture features is presented to distinguish between ships and nonships to remove most false alarms. In this way, we construct an salient object detection scheme that combines top-down
13、with bottom-up processing.3. After distinguishing between ships and nonships, the two-dimensional principal component analysis (2D-PCA) is introduced to enhance the representation ability of the feature set in feature extraction. Combing the HDR Tree, a supervised classification approach based on fi
14、ner texture features is presented to distinguish between civil cargo ships and military vessels. Experimental results on real remote sensing data show that the texture feature can be extracted effectively by using 2D-PCA and our method performs well in ship classification. Furthermore, the processin
15、g speed of our method is quite satisfying.Index Terms:Selective Visual Attention, Remote Sensing Imagery, Ship Detection and Recognition, Biquaternion, Fourier Transform, Shape Features, Texture Features, Hierarchical Discriminant Regression Tree (HDR Tree), Two-dimensional Principal Component Analy
16、sis (2D-PCA)【关键词】选择性视觉注意 遥感图像 舰船检测与识别 双四元数 傅里叶变换 形状特征 纹理特征 多层分类回归树 两维主成分分析【英文关键词】Selective Visual Attention Remote Sensing Imagery Ship Detection and Recognition Biquaternion Fourier Transform Shape Features Texture Features Hierarchical Discriminant Regression Tree (HDR Tree) Two-dimensional Principal Component Analysis (2D-PCA)【目录】基于选择性视觉注意机制的遥感图像舰船目标检测与识别 摘要 3-4 Abstract 4-5
