基于深度卷积网络和分水岭分割的耕地地块识别方法

耕地地块作为精准农业的重要支撑,现有地块边界大多依靠人工勾绘。随着遥感技术的发展,基于遥感影像自动提取耕地地块成为研究主要方向,其中基于深度学习的方法能够克服传统检测方法难以适应复杂场景的局限而被广泛使用,但现有检测方法仍存在问题,基于深度卷积模型直接识别耕地区域会丢失内部边界、而基于边缘检测模型识别耕地边界时则会同时得到大量无关边界;此外,现有的基于阈值提取地块的策略所提取的地块不够规整,存在内陷的问题。针对上述问题,本研究提出一种基于深度卷积网络和分水岭分割的耕地地块提取方法,从信息检测和地块提取两方面进行改进：(1)将耕地边界视作一种地物类别,在深度卷积网络中进行类别概率检测,帮助实现对耕地边界的语义识别。(2)基于改进后的D-LinknetXt网络进行检测,其网络架构适合于对耕地边界这类线性目标的提取,同时更换原始D-Linknet网络的残差单元,帮助提高了网络的特征提取能力。(3)基于分水岭分割对耕地地块进行提取,利用了区域分割方法获取边界的封闭性,并且这种以区域为单元进行分割并合并的方式,解决了原有方法在像元尺度上基于阈值提取所遇到的提取地块存在内陷的问题,使地块更规整准确...     

Underwater Target Detection Based on Reinforcement Learning and Ant Colony Optimization

Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target detection.However,low-light and high-noise scenarios pose great challenges in un-derwater image and video analyses.To improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this paper.First,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each pixel.These methods aim to avoid undetection and misdetection of some pixels in image edges.Second,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search abilities.Experimental results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.     

Automatic cell object extraction of red tide algae in microscopic images

Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms.This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images.Depending on the existence of setae,we classify the common marine red tide algae into non-setae algae species and Chaetoceros,and design segmentation strategies for these two categories according to their morphological characteristics.In view of the varied forms and fuzzy edges of non-setae algae,we propose a new multi-scale detection algorithm for algal cell regions based on border-correlation,and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects.In this process,similarity detection is introduced to eliminate the pseudo cellular regions.For Chaetoceros,owing to the weak grayscale information of their setae and the low contrast between the setae and background,we propose a cell extraction method based on a gray surface orientation angle model.This method constructs a gray surface vector model,and executes the gray mapping of the orientation angles.The obtained gray values are then reconstructed and linearly stretched.Finally,appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae.Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape,regular contour,and clear edge.Compared with other advanced segmentation techniques,our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.     

基于线段检测算法的倾斜摄影直角像控点目标检测方法

针对航空摄影测量中空三加密时数据内业精校正的人工刺点效率低下,误差较大等问题,本文提出了一种基于线段检测(Line Segment Detector,LSD)算法的直角像控点目标检测方法.该方法首先通过Retinex算法增强影像彩色信息;再对影像进行双边滤波,在去除噪声的同时能很好的保留边缘信息;然后使用LSD算法提取...     

土地利用动态监测的LFF图像融合改进应用

遥感是快速获取土地利用/土地覆盖现势信息的重要手段之一。由于不同类型的卫星传感器获取同一地区的遥感数据日益增多,对不同平台、不同光谱响应范围的遥感数据源,用常规的遥感图像融合方法如HSI变换法、Brovey变换法和PCA变换法进行融合时,存在不同程度的光谱扭曲现象,即融合后图像的色彩与多光谱图像存在较大差异,从而影响了地物的识别。针对不同平台、不同光谱响应范围的遥感数据,本文在HSI变换的基础上,提出了一种改进的方法,即,首先对高几何分辨率的全色波段进行LoG滤波,然后将LoG滤波后的全色波段与多光谱经HSI正变换后的强度分量,进行灰度直方图匹配,并替换之,经HSI逆变换便得到融合图像。分析结果表明：LFF融合法的光谱保持性能优于HSI变换法,LFF融合后图像的分类精度高于HSI融合后的图像,LFF融合法是一种能较好保持光谱特性的融合方法。     

基于网格模板的最小欧式距离准则图像自动拼接技术

提出一种结合网格模板和最小欧式距离准则的图像自动拼接方法。方法选取图像重叠部分作为处理对象，运用边缘检测自动获取基准图像特征块，并提出基于网格模板的最小欧式距离准则用于计算匹配点，最后使用图像融合技术消除接缝。应用证明该方法拼接准确快速，实现简单，适合大部分图像拼接应用的需要。     

数字钻孔全景影像中结构面特征智能识别方法

数字钻孔全景影像是识别深部岩体结构面的主要方法,传统解译方法主要依靠手动操作完成,其结果存在较强的人为性、主观性和较大误差。针对数字钻孔全景影像技术,提出了一种考虑结构面图像灰度分布特点的智能识别新方法。通过钻孔图像的灰度化、降噪预处理后利用结构面定位信号特征值D定位结构面,获取其所在区域图像;再进行边缘检测、阈值分割及形态学等处理,通过拟合边缘曲线并结合数学表征方法实现了结构面特征(产状、隙宽等几何参数)的智能化识别。通过对地下水封洞库工程深部岩体数字钻孔影像的实例分析,运用本智能识别方法获得结构面特征信息相对于传统人工识别方法,其结果的准确性和客观性更强、批量识别效率更高。这对这数字钻孔影像的智能、快速识别具有一定的参考价值。      

An Approach to Underwater Image Enhancement Based on Image Structural Decomposition

Underwater imaging posts a challenge due to the degradation by the absorption and scattering occurred during light propagation as well as poor lighting conditions in water medium. Although image filtering techniques are utilized to improve image quality effectively, problems of the distortion of image details and the bias of color correction still exist in output images due to the complexity of image texture distribution. This paper proposes a new underwater image enhancement method based on image structural decomposition. By introducing a curvature factor into the Mumford_Shah_G decomposition algorithm, image details and structure components are better preserved without the gradient effect. Thus, histogram equalization and Retinex algorithms are applied in the decomposed structure component for global image enhancement and non-uniform brightness correction for gray level and the color images, then the optical absorption spectrum in water medium is incorporate to improve the color correction. Finally, the enhanced structure and preserved detail component are recomposed to generate the output. Experiments with real underwater images verify the image improvement by the proposed method in image contrast, brightness and color fidelity.     

基于相似像素选择的双边滤波图像去噪

针对双边滤波在一定程度上模糊了图像特征的缺陷,提出一种基于相似像素选择的图像双边滤波算法。该算法首先在图像中搜索所处理像素的邻域内所有相似像素(与其像素值相近的像素),然后依据相似像素占全部邻域像素比例,确定该像素为特征像素或非特征像素。对于特征像素,采用其相似像素进行双边滤波;对于非特征像素,采用全部邻域像素进行双边滤波。仿真实验表明,该算法相对一般双边滤波,能够在去除噪声的同时,更好地保持图像特征。     

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise of remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.     

基于引力模型的海洋锋信息提取

海洋锋面是海洋水团特性明显不同的两种或几种水体之间的狭窄过渡带。本文旨在对遥感反演海洋温度场数据(SST),引入引力模型进行海洋锋面的检测。鉴于海洋锋受噪声干扰大,锋面强度小的特点,本文提出了基于引力算法的引力模型。其中,引力算法是将温度数据中的每一个像元点都作为一个独立的天体,其质量对应该像元的温度值,根据引力定律计算3×3区域中,邻域像元对中心点像元的引力和。模型首先对原始数据进行去0处理,为消除对原始数据明暗程度的依赖,对3×3区域数据进行归一化,然后利用函数对归一化后的数据进行增强处理,最后,以引力算法进行锋面检测。验证表明,该模型能有效强化不同区域或水体差异性,并能够有效针对海洋锋信息进行提取,受噪声影响小。     

分割暗通道先验邻域的单幅图像去雾算法

利用暗原色先验进行单幅图像去雾时,需采取高计算复杂度的细化程序,否则其估计的传输率易在边界处造成光晕。对导致边界处产生光晕现象的原因进行分析时发现,计算复杂度高的细化程序在去除晕轮效应时去雾过度,且传统的基于暗原色先验的单幅去雾算法在明亮区域易造成色彩失真现象。由此在原来的透射率估计时,提出一种基于色调的简单而快速的邻域分割方法。首先将原始RGB图像转换到HSI色彩空间,在H（Hue）通道中,用邻域中的点与中心点的色调的差值绝对值,来判断该邻域内的点是否属于同一区域,只使用属于同一区域的像素点来计算该区域的暗原色值;再通过修正透射率值,来校正明亮区域的色彩失真。在图像复原时,在HSI色彩空间保留色调分量不变,仅对强度分量运用修改的暗原色值进行去雾,再进行非线性增强,最后对饱和度分量进行颜色补偿。实验表明,本文的去雾算法能够显著提高场景的视觉清晰度,而且不需要图像后续修补,并能获得更好的色彩视觉保真。     

基于颜色特征和空间特征的图像检索

针对已有的综合多特征进行图像检索的算法复杂度高、计算量大且准确率不够的现状,提出一种基于图像分块并综合灰度特征和空间特征的图像检索算法。首先将原图像划分为10×10的子块,再计算每个子块的平均灰度和空间位置作为检索特征,通过实验表明这种方法在检索结构相似的图像时准确率高,但对颜色不敏感。通过增加颜色特征并调整空间特征计算方式进行改进,相比改进前,算法对于公开的图像库中大多图片有更高的查准率。实验中统计了多类图片在不同情况下的查准率,表明文中算法准确高效,应用性好。     

基于图像分割的城市变化检测

利用遥感数据进行城市变化检测时,由于城市是包含建筑物、道路、绿地以及水体等多种地物类型的综合体,同时与农村居民地等地物类型具有相似的光谱特征,因此,传统的单纯基于光谱信息的变化检测方法很难取得理想的效果。将空间信息加入到变化检测中,可提高变化检测的精度,但常用的加入纹理的方法容易产生边缘效应。本文提出一种基于图像分割的变化检测方法,在该方法中使用一种基于组分的多尺度形态学梯度,具有对噪声不敏感、边缘不会变厚等优点;同时与现有方法进行比较。实验结果表明,该方法在加入图像空间信息的同时避免了加入纹理等空间信息所产生的边缘效应,能够有效地提高城市变化检测的精度。     

基于多层神经网络与Sentinel-2数据的大豆种植区识别方法

大豆作为全球最重要的油料作物,是中国进口的大宗农产品,对其种植区的精准识别是决策制定、种植结构调整基础,对国家粮食安全有重要意义。本文利用Sentinel-2作为数据源,利用多层神经网络方法与对大豆进行提取,并与随机森林、决策树、支持向量机等机器学习进行对比,发现F1-Socre指标显示多层神经网络的分类精度最高,为93.53%,其次为随机森林、支持向量机、决策树。将神经网络分类结果与SLIC面向对象分割聚合之后,结果既忽略了同一地块的微小差别,又区分出了不同地块的作物差异,很好的体现了大豆的分布。Sentinel-2数据是进行大尺度大豆种植监测的绝佳数据源,大豆与玉米等其他作物在第二个红边波段的反射率有较为明显的差异。多层神经网络方法在图像分类任务中表现出色,结合图像分割算法精度可达到95.51%,可以满足大豆种植面积监测的需求。     

Study of formation boundary and dip attribute extraction based on edge detection technology

In the seismic profile interpretation process,as the seismic data are big and the small geological fea-tures are difficult to identify,improvement of the efficiency is needed.In this study,structure tensor method in computer image edge detection processing is applied into the 2D seismic profile.Coherent attribute is used to extract formation edge.At the same time,extracting the eigenvalues and eigenvectors to calculate the seismic geometric properties which include dip and apparent dip,automatic identification is achieved.Testing the Gaussian kernel function with synthetic models and comparing the coherent attribute and dip attribute extraction results before and after,the conclusion that Gaussian filter can remove the random noise is obtained.     

复杂背景下提高精度的眼睛定位方法

提出了一种在基于肤色的人脸检测中通过进行相似度计算来实现人眼特征提取和定位的算法.其目的是在较复杂的背景环境下对静态图片中的人脸进行检测,找出脸部区域并对该人脸进行眼睛定位.先进行肤色分割来完成人脸的区域检测,然后在检测出的人脸范围中利用人脸的几何特征进行快速的眼睛定位.在人脸区域检测过程中使用均值滤波提高人脸区域定位的准确度.实验结果证明,这种方法是准确的、可靠的和快速的.     

一维条码识别系统的设计与实现

一维条码识别系统是服务于手机识别系统的后台处理程序.系统完成对手机拍摄到的条码图片进行滤波、阈值分割、二值化、上下边沿检测、码字识别等功能.系统不仅能对正常的条码(EAN-13码)图片进行正确识别,而且还能对受到轻度污染和残缺的图片进行识别.     

结合像元级与对象级的滨海湿地变化检测方法

滨海湿地是动态且脆弱的生态系统,遥感变化检测技术为滨海湿地动态变化监测提供了有效手段。为解决像元级变化检测对配准误差敏感及其椒盐现象严重,对象级变化检测受分割参数影响较大且过程繁琐等问题,本文提出了显著图引导的结合像元级与对象级变化检测方法。首先,提取湿地亮度、归一化差异植被指数、归一化差异水体指数三个特征,得到特征差异影像;其次,利用最大对称环绕显著性检测算法生成显著图,采用结合模糊C均值和马尔可夫随机场方法对显著区域进行分割得到初始像元级变化检测结果;最后,在面向对象分割的基础上,通过构建对象的不确定性指数自适应选择训练样本,采用随机森林分类器进行分类得到最终变化检测结果。利用江苏盐城滨海湿地资源三号影像进行实验,结果表明,结合像元级与对象级方法的湿地变化检测总体精度为93.51%,与像元级、对象级方法相比,虚检率分别降低了29.04%和22.78%。     

Edge detection of potential field data based on image processing methods

The conventional methods of edge detection can roughly delineate edge position of geological bodies, but there are still some problems such as low detection accuracy and being susceptible to noise interference.In this paper, three image processing methods, Canny, LoG and Sobel operators are briefly introduced, and ap-plied to edge detection to determine the edge of geological bodies.Furthermore, model data is built to analyze the edge detection ability of this image processing methods, and compare with conventional methods.Combined with gravity anomaly of Sichuan basin and magnetic anomaly of Zhurihe area, the detection effect of image pro-cessing methods is further verified in real data.The results show that image processing methods can be applied to effectively identify the edge of geological bodies.Moreover, when both positive and negative anomalies exist and noise is abundant, fake edge can be avoided and edge division is clearer, and satisfactory results of edge detection are obtained.