【作者】 汪海洋；

【导师】 潘德炉； 夏德深；

【作者基本信息】 南京理工大学 ， 模式识别与智能系统， 2008， 博士

【摘要】 针对我国航空探测海洋中水下目标的需求,本文开发了一套基于航空可见光遥感的水下目标自动识别系统原型。该系统利用数码相机采集可见光信息,并对得到的图像信息用计算机进行实时处理,快速地提取目标信息,并能够得到目标的类型、大小等参数,解决了传统航空可见光侦察水下目标方法中的误判问题--传统的航空可见光侦察水下目标,主要通过飞机上望远镜观察海面异常水色形迹,由于海洋现象引起的水面异常的干扰,有时很难确定水下目标是否存在,目标的类型、大小等参数就更难确定了。当水下目标的信息在进入航空可见光遥感器(数码相机)时,由于水体的吸收和散射,只有很小一部分进入了遥感器,另外由于海洋中的随机扰动、太阳耀斑和低云阴影等强噪音的影响,使得图像中目标的信息很弱,噪音信息很强。本文要解决的问题是信息弱噪音强图像中的水下目标自动识别,论文的主要工作成果和创新点如下:1)本文提出了基于水下目标光谱特征和水体光谱特征的最佳探测水下目标的色彩空间选取方法。由于水下目标光谱反射率特征和水体光谱反射率特征不同,而水下目标光谱反射率特征和水下目标模型光谱反射率特征基本相同,所以水下目标模型和水体的光谱反射率特征差异近似于水下目标和水体的光谱反射率特征差异。因此,该方法通过测量水体和水下目标模型不同深度的光谱反射率数据,分析光谱反射率特征曲线,求出它们之间光谱反射率差值最大的波段,得到最佳的探测波段并由此推导出最佳的色彩空间。试验结果表明无论从物理机制还是从实际的图像效果上都证明了该算法的有效性。2)本文提出了二维otsu阈值分割的快速算法。Otsu自适应阈值算法作为图像阈值分割的经典算法,在图像领域得到了广泛的应用,在此基础上发展起来的二维阈值算法因为计算时间长而制约了其应用。针对二维otsu自适应阈值算法计算复杂度高的缺点,通过消除二维自适应阈值算法中的冗余计算,用迭代的方式得到查询表,从而大大提高了二维阈值算法的计算速度。本文算法不仅计算时间远远小于原始二维otsu算法,并且求得的阈值跟原始算法一样。3)本文提出了基于Fisher分离准则和二维otsu阈值的高亮度区域分割算法。该方法首先将图像分割大小相同的子图像,并用二维ostu阈值分割算法将子图像分为两类。然后使用Fisher线性鉴别准则来判断分类结果是可信,即子图像是否包含目标和背景,如果可信,将求得的阈值作为子图像的阈值;否则,认为子图像的阈值空。最后,将阈值为空的子图像的邻域子图像阈值平均值作为该子图像的阈值,进行迭代运算直到每个子图像都有阈值,二值化整幅图像。试验结果表明,本文的方法能有效地分割出水下目标运动形成的浪法和耀斑等高亮度区域,消除了光照变化、云阴影等噪音的影响。4)本文提出了一种基于变形模板的水下目标轮廓恢复算法。由于受噪音和太阳耀斑等的影响,无法直接分割得到完整的目标轮廓,而在实际的应用中我们需要知道水下目标具体的位置和大小,为此根据目标形状已知,本文重新定义变形模板的能量函数,并分别用梯度下降法、模拟退火算法、遗传算法和免疫算法来求解能量函数的最优解,进而求得水下目标的准确边缘轮廓和大小。详细比较了四种最优算法在该问题上的性能,试验结果表明免疫算法求得的能量函数最优解最为准确和稳定。5)本文提出了基于方向Gabor滤波特征的主成分分析与独立分量分析的尾迹纹理检测算法和基于方向傅立叶极频谱的二维主成分分析的尾迹纹理自动检测算法。在研究了大量纹理特征分析算法的基础上,根据条纹纹理方向强的特点,本文提出了加权协方差矩阵算法,准确地求取了条纹纹理方向,并分别提出了基于方向Gabor滤波器的主成分分析与独立分量分析的纹理特征分析算法,和基于方向傅立叶极频谱的二维主成分分析的纹理特征分析算法。为了有效地对纹理特征进行分类,本文学习和研究了统计学习理论和支持向量机的原理与实现算法,并将它作为纹理特征的分类器。大量的实验表明本文的方法能够有效地检测水下目标运动产生的尾迹纹理。为了验证本文算法识别航空可见光遥感图像中水下目标的有效性,对目前获得的实验图像数据进行了测试,得到了很高的识别率,但是由于海洋中水下目标信息很弱,并且受海洋中随机扰动、太阳耀斑和云阴影等噪音的影响,本文开发的水下目标自动识别算法要推广到实际的应用中还需大量的实验并不断地完善。更多还原

【Abstract】 To satisfy the need of detecting ocean underwater target by aerial visible light remote sensing, this paper develops an underwater target automatic recognition system prototype. The system prototype collects visible light information by digital camera, processes images by computer real-timely, obtains underwater target information quickly, and reduces error of traditional method. The traditional underwater target monitoring is mainly accomplished by observing ocean color abnormity from telescope. Sometimes, it is very difficult to detect underwater target and computer their parameter such as type, shape and so on for ocean phenomena disturbance. Because of absorbing and scattering of the water, a few fraction of underwater target information is received by the remoter sensor. Otherwise, ocean random disturbance、sun flares and cloud shadows induce target information weak while induce noisy information robust. The difficult problem of this paper is to recognize weak information underwater target on the condition of robust information noisy. The primary achievement and innovation are as follow.1) This paper proposes the optimal color space selection for underwater object recognition based on water spectrum feature and object spectrum feature. It is different between spectrum reflectivity of underwater object and water, while it is nearly same between spectrum reflectivity of underwater object and object model. Thus it is reasonable to study object model spectrum feature to simulate underwater object spectrum feature. A lot of experiments are done to measure spectrum reflectivity of object model and water. The spectrum reflectivity curves are analyzed to find wave band with the most reflectivity difference between underwater object and water and achieve the optimal color space. The validity of selected color space is proved from physics mechanism and experiment images.2) This paper proposes a fast algorithm for two-dimensional otsu adaptive threshold algorithm. As a classical image segmentation method, otsu adaptive threshold algorithm has applied widely in image processing. The application of the two-dimensional otsu threshold algorithm based on the otsu threshold algorithm has been restricted for the long-paying computation. This paper gives a fast algorithm for two-dimensional otsu adaptive threshold algorithm that overcomes the disadvantage of high computational complexity. This fast algorithm gets rid of redundance computation and yields a look-up table by iteration. The computational time of the fast method is not only far less than that of the source two-dimensional one, but also yields the same threshold and the original method.3) This paper proposes the high illumination area threshold algorithm based on fisher separation criterion and two dimensional otsu threshold. First, the whole image is divided into sub-images with the same size. Second, the sub-image is segmented into two classifications by two dimensional otsu threshold. The segmentation is validated by the fisher criterion to determine whether the sub-image threshold is empty. Finally, the sub-image with empty threshold is set to average threshold of its neighbor sub-images. From experiments, the method can availably segment high illumination area of sprays and sun flares to eliminate cloud shadows and illumination variety.4) This paper proposes an edge restoration algorithm of underwater target based on deformable templates. Because of sun flares, noisy and so on, it is mostly impossible to directly extract the edge contour of underwater target. However, it is necessary to confirm position and size of the underwater target in practical application. Thus we define the energy function of deformable templates anew according to underwater target shape prior information. Gradient descending, simulate annealing, genetic algorithm and immunity algorithm are used to solve the minimum value of energy function. From a lot of experiments, the immunity algorithm precedes other three algorithms.5) This paper proposes a wake detection method for the water wake of airphotoes using independent component analysis of Gabor features and based on two-dimensional principal component analysis (2DPCA) of directional polar Fourier spectrum. Based on a lot of texture analysis algorithm, according to the strong direction performance of strip texture, this paper proposes the weighted covariance matrix to obtain the strip texture orientation more precisely. Also texture analysis algorithms based on principal component analysis, independent component analysis of Gabor features and based on two-dimensional principal component analysis of Directional Polar Fourier Spectrum. Statistical learning theory and support vector machine are studied thoroughly to be the classification of texture features. From lots of experiment results, it is proved that the proposed algorithm can extract wake texture of underwater target precisely.To validate our algorithm of detecting underwater targets in aerial visible light remote sensing images, many experiment images are done to achieve very high correct percent. However, there are many noisy in the ocean such as random disturbance, sun flares, cloud shadows, and weak underwater target information. So more experiments are needed to perfect an underwater target automatic recognition system prototype and practice the prototype.更多还原