融合阈值分割和注意力网络的建筑阴影检测

针对高分辨率高层建筑物遥感影像噪声干扰大、阴影检测困难的问题,本文提出了一种改进阈值分割和注意力残差网络结合的高层建筑物遥感影像阴影检测方法.首先,利用改进最大类间和最小类内阈值分割算法建立阈值分割模型,并基于轮廓间的连通域特性和端点位置约束关系利用欧几里得度量算法对断裂轮廓进行修补得到阴影轮廓;然后,利用生成对抗网络模型对误判数据集进行扩充;最后,对残差网络进行改进,在特征图中加入注意力机制进行全局特征融合.在不同场景下,分别与辐射模型、直方图阈值分割、彩色模型阴影检测方法,支持向量机、视觉几何群网络、Inception和残差网络分类网络进行了对比实验,本文方法综合误判率和漏检率分别为2.1%、1.5%.结果表明,本文提出的高层建筑遥感阴影检测算法能较好地完成阴影区域的分割和检测,有利于节约人力物力资源、协助工作人员进行遥感信息的解译、遥感档案建立等工作,具有实用价值.     

一种基于边缘特征的海岸线检测方法

针对遥感图像中灰度特征的不稳定性,尤其是SAR（合成孔径雷达）图像中水陆灰度的弱对比性,提出一种利用相对稳定的边缘特征进行海岸线检测的新方法。该方法首先用Roberts算子提取海岸区域图像的梯度,并进行自动阈值分割,进而用轮廓跟踪的方法得到海岸线的粗略位置。借鉴主动轮廓的思想,设计一种轮廓逼近的方法,根据遥感图像的梯度强度对粗略的海岸线进行调整,最终得到精确的海岸线。实验表明这种方法在检测精度和抑制海域内干扰等方面都能达到令人满意的效果。     

基于最小错误率的SAR图象分割方法研究

文章提出了一种基于最小错误率的SAR图象自动分割算法。对直方图呈现出多模分布特征的SAR图象,首先运用有限高斯混合分布对SAR图象特征空间的数据统计模型进行估计;其次基于最小错误率原理选取SAR图象自动分割阈值,在先验概率未知和估计条件下,获得目标及其阴影区域的检测结果;最后对两种图象分割结果进行了分析并与SAR图象目标检测的经典方法-恒虚警(CFAR)目标检测方法作了比较。仿真结果表明在先验概率估计下的图象自动分割具有明显的优势和较大的应用潜力。     

基于小波分形特征提取的图象分割方法

提出了一种基于小波分解和分形纹理特征计算的图象分割方法,首先考虑对图象进行小波变换,然后对不同通道的子图象提取纹理的分形特征和能量特征,最后用直方图阈值分割方法实现图象的分割,实验表明,该方法对模拟纹理图象以及多少谱遥感图象的分割都取得了满意的效果。     

微目标遥感图像的感兴趣区域提取新方法

结合微目标遥感图像的灰度、轮廓、大小等信息特征,提出了一种基于视觉注意的微目标遥感图像感兴趣区域提取方法,首先采用形态学Top-Hat变换强化感兴趣区域和抑制背景,利用开运算实现去除噪声和虚假微目标:然后通过阈值迭代法初步分割出感兴趣区域;最后通过人工交互,结合形态学方法进行感兴趣区域提取,进而运用基于小波变换的多尺度边缘检测算法探测出感兴趣区域的边界.实验结果表明:该方法能快速有效地分割提取出微目标感兴趣区域.     

结合多种特征的高分辨率遥感影像阴影检测

针对现有的阴影检测算法对较亮阴影和较暗地物中的阴影不能同时较好地检测等问题, 提出一种结合多种特征的高分辨率遥感影像阴影检测方法.该算法首先结合主成分分析、颜色特征和直方图的分割构建多种阈值检测条件, 然后综合多种特征来进行遥感影像阴影的初步检测, 最后通过分析RGB模型在阴影与非阴影地物上的差别, 利用颜色特性最终检测出阴影区域.实验结果表明, 本文算法能有效检测较亮阴影和较暗地物中的阴影.与现有方法相比, 较亮阴影的平均总错误率从水平集法的31.85%降至24.61%, 较暗地物中阴影的平均总错误率从自动检测法的37.75%降至23.30%.     

一种肺部肿瘤CT图象序列的自动分割方法

肺部肿瘤序列图象的自动分割是计算机肺部肿瘤三维辅助诊断系统的关键技术之一,肿瘤与周围组织关系的复杂性造成分割困难.为了给医生提供准确的肺部肿瘤影像,运用纹理分析和径向基神经网络实现了肺部肿瘤CT图象序列的自动分割,并根据相邻层肿瘤图象灰度、位置的相关性,提出了一种自动获取多层肿瘤区域神经网络训练样本的阈值分割算法.该算法首先计算图象纹理统计参数,以组成特征矢量空间,然后利用自适应径向基神经网络对特征矢量进行分类来实现肿瘤序列图象的自动分割.实验结果表明,与基于灰度的区域增长法和基于梯度算子和形状算子的最优阈值的分割方法相比较,该方法不仅能充分利用肺部肿瘤序列图象的三维信息,还可最大限度地减少人工干预,且分割结果较好地表现了肿瘤形态特征,经临床医生评估,具有较好的临床指导价值.     

自动分割视频序列中运动物体的新算法：多特征联合方法

视频编码标准MPEG-4增加了适于多种应用的基于视频内容的功能,为了支持这一功能和提高编码效率,MPEG-4将视频序列中的每一帧分解成视频对象面(VOP);另外,由于基于内容的视频检索和视频监控系统均期望用分割出的关键视频对象紧致地表示一个序列,同时由于视频分割技术在模式识别、计算机视觉等领域也得到了广泛的应用,因此,分割视频运动物体并跟踪运动物体的变化变得至关重要.为了对视频中运动物体进行有效的分割,在帧差图象的基础上,采用Canny边缘检测和随机信号的高阶矩检测相结合的方法,来自动分割视频序列的前景区域和背景区域,并在前景区域中应用区域生长法进行颜色分割,以精确提取运动物体的边缘;还利用边缘和颜色特征来对分割出的运动物体建立模板,用于解决非刚体运动中局部暂时停止运动的情况.实验结果表明,此方法可以有效地分割运动物体,并能跟踪运动物体的变化.     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题,通过分析高分二号影像中典型地物的光谱特征,构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括:主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理,建立包含波段均值、标准差等特征的规则集,对影像的I和PC1分量进行多尺度分割,结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验,实验结果表明:与传统基于像素的阴影提取方法相比,该方法提取出的阴影斑块完整,且能有效地减弱水体和植被的影响。     

基于自适应定向正交投影分解的图象分割方法

将目标和背景分别对应到灰度直方图中的两个高斯分布是进行图象分割的一种常用方法 ,但复杂图象的直方图往往是多峰的 .为了更好地拟合这种复杂图象直方图的多峰特性 ,提出了一种基于自适应定向正交投影分解的图象分割方法 .该方法首先将这种复杂图象的直方图看作是多个高斯分布的叠加 ,并可通过应用自适应定向正交投影分解法来快速准确地确定每个高斯分布的权值、均值和方差 ,进而计算出各相邻高斯分布之间的最优阈值 ,以用于图象分割 .在此基础上 ,又提出了阈值分离度的概念 ,并将其作为选取最终阈值的指标 .应用实例结果表明 ,该方法能够快速有效地实现复杂图象的多阈值分割 .     

结合局部分类水平集与颜色特征的遥感影像阴影检测

为提高对非匀质阴影与亮阴影的检测效果,提出结合局部分类水平集与颜色特征的遥感影像阴影检测方法,首先,结合阴影区域的亮度非均匀性,采用局部分类水平集分割遥感图像的阴影区域;然后,通过分析绿地与阴影颜色特征分量的差别以去除候选阴影区中被误检的绿地.实验结果表明所提出的方法优于现有的黑体辐射模型与自适应特征选择法,有效克服了传统方法对非匀质阴影与亮阴影的漏检问题,且整个检测过程无需人工干预.     

Shadow Segmentation and Classification in a Constrained Environment

A shadow identification and classification method for real images is developed in this paper. The method is based on the extensive analysis of shadow intensity and shadow geometry in an environment with simple objects and a single area light source. The procedure for identifying shadows is divided into three processes: low level, middle level, and high level. The low level process extracts dark regions from images. Dark regions contain both shadows and surfaces with low reflectance. The middle level process performs feature analysis on dark regions, including detecting vertices on the outlines of dark regions, identifying penumbrae in dark regions. classifying the subregions in dark regions as self-shadows or cast shadows, and finding object regions adjacent to dark regions. The high level process integrates the infonnation derived from the previous processes and confirms shadows among the dark regions.     

A new shadow detection index for 8-band very high-resolution satellite images

ABSTRACT

The new generation of remote sensing satellite with very high-resolution images has provided a high level of details, which make them a reliable source of information. Presence of shadow can reduce the amount of information that can be extracted from these images. Shadow can be confused with dark objects such as water and dark vegetation. The main aim of this research is to develop a new index to detect shadow in the presence of dark objects using the capabilities of the new remote sensing satellite images. For this study, WorldView-2 (WV-2) remote sensing satellite images with eight spectral bands were used. A spectral reflectance analysis for the main ground features has been studied along the eight spectral bands to determine the most effective bands for shadow detection. These bands are employed with the Hue-Saturation-Intensity colour model for producing the new proposed Saturation Intensity Shadow Detection Index (SISDI). The proposed index is applied to four study areas and compared with two state-of-the-art indices of shadow detection. Results of this comparison demonstrate the more accuracy effectiveness and feasibility of that proposed index. The proposed index achieves the highest overall accuracy (average of 97.8%) and has the ability for detecting small shadow areas.     

SAR图像中海上舰船目标自动检测新方法

针对中分辨率近岸海域SAR图像,结合已有的舰船检测算法,提出了一种新的海上舰船目标自动检测方法。该方法先根据相应的抽取算法和图像数据映射准则,分离图像中的海洋和陆地区域,并结合最大熵分割法提取海洋背景中包含候选目标的感兴趣区域,最后利用特征匹配方法检测出真正的舰船目标。对50多幅SAR图像进行了试验,其结果表明该方法能自动、快速、准确地检测出图像中舰船目标。     

基于照度补偿的人脸图像遮挡阴影消除处理*

为了消除人脸图像中的遮挡阴影对识别精度的影响,采用数学形态学处理对阴影进行检测分离,根据其照度损失不同划分为阴影边缘区和阴影主体区,分别实施照度补偿,并将非阴影区、阴影边缘区和阴影主体区光强光顺过渡,实现对图像阴影区域的恢复处理。基于该照度补偿原理处理实例表明,该方法可以较好地去除人脸图像中部因遮挡造成的阴影,显著改善图像质量。     

Efficient Shadow Removal Using Subregion Matching Illumination Transfer

This paper proposes a new shadow removal approach for input single natural image by using subregion matching illumination transfer We first propose an effective and automatic shadow detection algorithm incorporating global successive thresholding scheme and local boundary refinement. Then we present a novel shadow removal algorithm by performing illumination transfer on the matched subregion pairs between the shadow regions and non‐shadow regions, and this method can process complex images with different kinds of shadowed texture regions and illumination conditions. In addition, we develop an efficient shadow boundary processing method by using alpha matte interpolation, which produces seamless transition between the shadow and non‐shadow regions. Experimental results demonstrate the capabilities of our algorithm in both the shadow removal quality and performance.     

Shadow detecting using particle swarm optimization and the Kolmogorov test

An algorithm combining both gray level information and geometric features is introduced to detect cast shadows in gray level images. A simply connected candidate shadow region and a corresponding region are segmented by setting gray level thresholds, and neighbor-matching regions are constructed with a mathematical morphological algorithm. A shadow-non-shadow region pair is obtained from the result of Kolmogorov test for statistical features of both candidate neighbor-matching regions. Shadow regions are obtained by selecting the region with relatively lower average gray level from the matched region pair. The particle swarm optimization (PSO) algorithm is used to facilitate the feature extraction during the matching process. Experimental results showed the effectiveness of the proposed algorithm for cast shadow detecting in a single gray level image.     

基于暗通道原理从单幅遥感影像估计山脉高程的方法

根据单幅遥感图像估计高程信息可以应用于滑坡、泥石流等自然灾害的检测,因此,提出了一种基于暗通道原理的单幅遥感图像高程值提取算法,并对山脉的阴影部分进行了改进。运用了暗通道原理,同时给出了一种克服山脉阴影影响的解决策略。对大量山脉遥感图像进行了定量和定性分析,提出的算法能够得到较准确的山脉相对高程信息。综上所述,改进算法能够有效提取出包含阴影的单幅山脉遥感图像的高程信息。     

An automatic shadow detection method for high-resolution remote sensing imagery based on polynomial fitting

Most existing shadow detection models and algorithms require extensive calculations and have difficulties effectively removing features, such as water bodies, some dark objects and bluish ground objects. In this paper, we propose a high-resolution automatic shadow extraction algorithm based on the process of histogram fitting. First, the histogram of the whole image is fitted by fourth and fifth-degree polynomials according to the histogram difference of the near-infrared bands of different shadow areas in the remotely sensed image. Second, the shadow area is preliminarily extracted based on the relationships between the shadow features of the remote sensing image and the intersections of the fourth- and fifth-degree polynomials. Then, the normalized difference water index (NDWI) is applied to extract the water bodies. Finally, to obtain the shaded area, the scanning line seed filling algorithm is applied to remove the water bodies falsely detected as shadows in the preliminary shading extraction. The proposed algorithm is evaluated by using the various high-resolution images including GaoFen-1 (GF-1), GaoFen-2 (GF-2), QuickBird2, and ZiYuan-3 (ZY-3), as well as an elaborate comparison to histogram threshold segmentation algorithms such as Component 3 (C3) algorithm, multi-elements extraction algorithm multi-band detection algorithm, and spectral correlation algorithm based on spectral features. The results of experiment showed that the proposed algorithm could extract the shadows of various images, achieve satisfied results, and completely remove water bodies.     

Seasonal and interannual variability of oceanographic processes in the Gulf of Guinea: An investigation using AVHRR sea surface temperature data

The Gulf of Guinea is situated in a critical position for understanding Atlantic equatorial dynamics. This study investigates seasonal and interannual variability in sea surface temperature (SST) throughout this region, focusing on dynamical ocean processes. A 10.5-year time series of remotely sensed SST data with 4 km spatial resolution from the Advanced Very High Resolution Radiometer (AVHRR) were used for this investigation, as they are sufficient to resolve shelf processes. Firstly, patterns of cloud cover were assessed, then spatio-temporal variability in SST patterns was investigated. Features identified in climatological SST images were the Senegalese upwelling influence, coastal upwelling, tropical surface water, river run-off and fronts. Of particular interest is a shelf-edge cooling along the coast of Liberia and Sierra Leone in February. Interannual variability, assessed using annual mean images, time series decomposition and spectral analysis, showed a quasi-cyclic pattern of warm and cool years, perhaps related to El Niño-type forcing. The results of this study show the usefulness of infrared remote sensing for tropical oceanography, despite high levels of cloud cover and atmospheric water vapour contamination, and they provide evidence for theories of westward movement of the upwelling against the Guinea current and remote forcing of the upwelling.