基于Landsat 8卫星影像的北京地区土地覆盖分类

目的 土地覆盖分类能为生态系统模型、水资源模型和气候模型等提供重要信息,遥感技术运用于土地覆盖分类具有诸多优势。作为区域性土地覆盖分类应用的重要数据源,Landsat 5/7的TM和ETM+等数据已逐渐失效,Landsat 8陆地成像仪(OLI)较TM和ETM+增加了新的特性,利用Landsat 8数据进行北京地区土地覆盖分类研究,探讨处理方法的适用性。方法 首先,确定研究区域内土地覆盖分类系统,并对Landsat 8多光谱数据进行预处理,包括大气校正、地形校正、影像拼接及裁剪;然后,利用灰度共生矩阵提取全色波段纹理信息,与多光谱数据进行融合;最后,使用支持向量机(SVM)进行分类,获得土地覆盖分类结果。结果 经过精度评价和分析发现,6S模型大气校正和C模型地形校正预处理提高了不同类别之间的可分性,多光谱数据结合全色波段纹理特征能有效提高部分地物的土地覆盖分类精度,总体精度提高2.8%。结论 相对于Landsat TM/ETM+数据,Landsat 8 OLI数据新增特性有利于土地覆盖分类精度的提高。本文方法适用于Landsat 8 OLI数据土地覆盖分类研究与应用,能够满足大区域土地覆盖分类应用需求。     

结合纹理特征的SVM样本分层土地覆盖分类

支持向量机(SVM)分类在精度、泛化性、高维数据处理等方面都具有较强的优势,在遥感影像分类中也得到了广泛应用。由于遥感影像“同物异谱”和“异物同谱”现象的影响,结合纹理特征提高SVM分类精度已成为遥感应用研究的热点。但不同尺度的纹理特征突出的信息不一,在同一尺度上难以区分的地物在多尺度空间则更容易区分,因此,采用多尺度纹理特征进行SVM分类,并从分类样本和纹理特征的选取两个方面探讨SVM土地覆盖分类的方法。首先,以ALOS影像为例,通过灰度共生矩阵提取不同尺度、不同方向的几种纹理特征;然后在光谱分类结果基础上,借助地类特征曲线,选取有效的多尺度纹理特征,最后进行样本分层分类。样本分层分类是选取首层样本进行分类,再从“漏分和错分”地块中选取新样本加入到首层样本中,得到第二层样本并对整个影像进行分类;用同样的方法选出第三层样本或更高层样本进行分类,直到结果满意为止。结果表明:该方法比仅用光谱特征的SVM分类总精度提高了8.11%,Kappa系数增加了0.11。其中,纹理特征的引入使分类总精度提高了4.13%,且对纹理特征较明显的地类更有效;采用样本分层后的分类总精度进一步提高了3.98%,且各单一地类的精度也都有不同程度的提高。借助地类特征曲线选择合适的纹理特征具有一定的可行性,并且采用样本分层的方法能够提高SVM分类的精度。     

基于CNN的不同空间分辨率影像土地覆被分类研究

基于卷积神经网络（Convolutional Neural Networks, CNN）和5种不同空间分辨率的遥感影像,对西宁市东部一区域开展土地覆被分类研究,旨在探索CNN在不同空间分辨率下进行影像分类的差异性和对不同地物的提取能力。为提高样本的选择效率,引入了窗口滑动方法进行辅助选样。研究表明5种不同空间分辨率影像的总体分类精度均达89%以上,Kappa系数达0.86以上,分类精度较高。在所涉及的分辨率尺度范围内,空间分辨率越高,CNN分类结果越精细,并能保持较高的分类精度,表明CNN更适合高空间分辨率影像分类;但同时影像空间分辨率越高,地物表现出较高的类内变异性和低类间差异性,分类精度有降低的趋势。相比较而言,SPOT 6影像的分类精度最高,同时窗口滑动是一种有效的样本辅助选择方法。研究对今后同类工作具有一定的借鉴意义。     

基于CNN的吉林一号卫星城市土地覆被制图潜力评估

以吉林一号视频07B星高分遥感影像为基础,采用卷积神经网络（CNN）对城区土地覆被进行精细分类,设置多组光谱变量集合,并与最大似然法、多层感知机和支持向量机分类方法进行对比,全面评估分析各方法对城区土地覆被信息提取的适用性及波谱特征对分类精度的影响。结果表明：CNN模型的分类精度最高,总体精度高于90%,相比其他方法提高幅度达12%以上,能够显著降低“椒盐”噪音;红边波段对所有方法总体分类精度贡献十分有限,而近红外波段对分类精度的提升较为明显;总体而言,红边和近红外波段对CNN分类精度影响较为微弱。深度学习应用于吉林一号高分遥感数据能获取高精度城区土地覆被分类图,可为城市土地资源配置,城市规划与管理提供重要的支撑。     

基于多分类器集成的“北京一号”小卫星遥感影像分类研究

将多分类器集合应用于"北京一号"小卫星多光谱遥感数据土地覆盖分类,首先构建分类器集合,应用最小距离分类、最大似然分类、支持向量机(SVM)、BP神经网络、RBF神经网络和决策树等进行土地覆盖分类,然后利用Bagging、Boosting、投票法、证据理论和模糊积分法等分类器集成方法,得到综合不同分类器输出的最终分类结果。试验表明,多分类器集成能够有效提高"北京一号"小卫星土地覆盖分类的精度,具有广泛的应用前景。     

Efficient Gabor filter design for texture segmentation

Gabor filters have been successfully applied to a broad range of image processing tasks. The present paper considers the design of a single filter to segment a two-texture image. A new efficient algorithm for Gabor-filter design is presented, along with methods for estimating filter output statistics. The algorithm draws upon previous results that showed that the output of a Gabor-filtered texture is modeled well by a Rician distribution. A measure of the total output power is used to select the center frequency of the filter and is used to estimate the Rician statistics of the Gabor-filtered image. The method is further generalized to include the statistics of postfiltered outputs that are generated by a Gaussian filtering operation following the Gabor filter. The new method typically requires an order of magnitude less computation to design a filter than a previously proposed method. Experimental results demonstrate the efficacy of the method.     

基于纹理方向的描述和对具有方向性纹理图象的分类与分割

为进一步进行纹理特征分析,从纹理的方向性入手,给出了纹理方向的数学定义式,合理选择差异函数,构造了具有物理意义的纹理方向描述特征向量,数据处理方面,运用模糊贴近度的概念,结合改进后的属性均值聚类算法,对一类具有方向性的纹理图象进行分类与分割实验,取得了较好的结果,试验表明,该方法对纹理的方向性有很好的描述能力。     

Segmentation using Appearance of Mesostructure Roughness

This paper introduces mesostructure roughness as an effective cue in image segmentation. Mesostructure roughness corresponds to small-scale bumps on the macrostructure (i.e., geometry) of objects. Specifically, the focus is on the texture that is created by the projection of the mesostructure roughness on the camera plane. Three intrinsic images are derived: reflectance, smooth-surface shading and mesostructure roughness shading (meta-texture images). A constructive approach is proposed for computing a meta-texture image by preserving, equalizing and enhancing the underlying surface-roughness across color, brightness and illumination variations. We evaluate the performance on sample images and illustrate quantitatively that different patches of the same material, in an image, are normalized in their statistics despite variations in color, brightness and illumination. We develop an algorithm for segmentation of an image into patches that share salient mesostructure roughness. Finally, segmentation by line-based boundary-detection is proposed and results are provided and compared to known algorithms.     

一种改进的基于样例的图像修复方法

以基于样例修补的目标移除方法为基础,改进了基于样本块的图像修补方法。在自然图像上通过傅里叶变换,发现许多自然图像具有方向性纹理的特征,将搜索空间约束到纹理方向的范围,优化了Criminisi方法优先块的选取,提高了搜索精度;并通过在源图像区使用图像分割的方法实现分区,使搜索目标块仅在其相邻的源区域内搜索,进一步缩小样本图搜索范围,增强搜索的准确性。在自然图像上进行的实验结果表明:改进的方法不仅显著提高了图像修补的时间,且有效地维持了图像的线性结构,取得了良好的修补效果。     

傅氏变换的自配准性质及其在纹理识别和图象分割中的应用

图象按纹理一致性进行辨识和分割是图象分析中的一个重要问题 ,有着广泛的实际应用 .讨论了傅氏变换应用于纹理识别的机理 ,并基于此提出了一种图象分割算法 .图象中的纹理线条呈现出很多方向 ,并随机地分布在图象的各个位置 ,然而对于它的傅氏变换幅度谱来说 ,相同方向的线条无论其位置如何 ,它们的贡献会被叠加在一起 ,集中地反映在通过频谱中心垂直于原线条方向的条带上 .这一现象被称为傅氏变换幅度谱的自配准性质 .首先对这一性质进行实验个例的研究和理论分析 ,然后设计算法将其应用于图象的纹理辨识和基于纹理的图象分割实验 ,取得了较为满意的效果 .实验证明 ,得益于自配准性质 ,傅氏变换方法不失为一种有潜力的纹理分析和图象分割方法 ,值得进一步扩展更多的图象应用领域     

基于灰度值统计特征的数字水印方法

提出了一种基于图像分块灰度值统计特征的数字水印方法,算法利用图像分块的灰度差异值判定水印信息的嵌入位置,对选定的图像分块进行DCT变换,然后将水印信息嵌入到中频系数当中。由于水印信息嵌入到了纹理复杂度因予比较大的块中,因此就增强了水印信息的不可见性。实验表明,本算法较好地保持了原始图像的品质,对非几何攻击和有损压缩具有较好的稳健性。     

On the optimality of image processing pipeline

The optimisation of image processing steps such as segmentation and feature extraction individually in an application does not yield an optimal pipeline. In this paper we demonstrate how the use of different image segmentation algorithms directly impacts upon the quality of texture measures extracted from segmented regions and final classification ability. The difference between the best and the worst possible performances by choosing different algorithms is found to be significant. We then develop the methodology for determining the optimal pipeline for scene analysis and show our experimental results on the publicly available benchmark “MINERVA”.     

A modified support vector machine and its application to image segmentation

Recently, researchers are focusing more on the study of support vector machine (SVM) due to its useful applications in a number of areas, such as pattern recognition, multimedia, image processing and bioinformatics. One of the main research issues is how to improve the efficiency of the original SVM model, while preventing any deterioration of the classification performance of the model. In this paper, we propose a modified SVM based on the properties of support vectors and a pruning strategy to preserve support vectors, while eliminating redundant training vectors at the same time. The experiments on real images show that (1) our proposed approach can reduce the number of input training vectors, while preserving the support vectors, which leads to a significant reduction in the computational cost while attaining similar levels of accuracy. (2)The approach also works well when applied to image segmentation.     

An object-based image retrieval system for digital libraries

A novel approach to clustering for image segmentation and a new object-based image retrieval method are proposed. The clustering is achieved using the Fisher discriminant as an objective function. The objective function is improved by adding a spatial constraint that encourages neighboring pixels to take on the same class label. A six-dimensional feature vector is used for clustering by way of the combination of color and busyness features for each pixel. After clustering, the dominant segments in each class are chosen based on area and used to extract features for image retrieval. The color content is represented using a histogram, and Haar wavelets are used to represent the texture feature of each segment. The image retrieval is segment-based; the user can select a query segment to perform the retrieval and assign weights to the image features. The distance between two images is calculated using the distance between features of the constituent segments. Each image is ranked based on this distance with respect to the query image segment. The algorithm is applied to a pilot database of natural images and is shown to improve upon the conventional classification and retrieval methods. The proposed segmentation leads to a higher number of relevant images retrieved, 83.5% on average compared to 72.8 and 68.7% for the k-means clustering and the global retrieval methods, respectively.