基于多元局部二值模式的遥感图像纹理提取与分类

纹理信息已经广泛应用于遥感图像分类以提高地物识别的精度。为了描述多光谱遥感图像多个波段之间的空间信息变化规律,将新型纹理提取算法局部二值模式（Local Binary Pattern,LBP）扩展到多维空间以计算多元纹理。单波段纹理信息、多元纹理信息分别与光谱信息结合后用于遥感图像分类,并根据分类精度评价其有效性。实验表明,加入单波段或多元纹理信息的分类精度均比光谱分类有明显提高;基于多元LBP纹理的分类不仅避免了传统单波段纹理参与分类前进行波段选择的繁琐,其精度还能与基于单波段纹理分类精度最高者相当或者更高。     

基于自动子空间划分的高光谱数据特征提取

针对遥感高光谱图像数据量大、维数高的特点,提出了一种自动子空间划分方法用于高光谱图像数据量减小处理。该方法主要包括3个处理步骤:数据空间划分,子空间主成分分析和基于类别可分性准则的特征选择。该方法充分利用了高光谱图像各波段数据之间的局部相关性,将整个数据划分为若干个具有较强相关性的独立子空间,然后在子空间内利用主成分分析进行特征提取,根据各类地物间的类别可分性选择有效特征,最后利用地物分类来验证该方法的有效性。实验结果表明,该方法能够有效地实现高光谱图像数据维数减小和特征提取,同现有的自适应子空间分解方法和分段主成分变换方法相比,该方法所提取的特征用于分类时能获得较好的分类精度。利用该方法进行处理,当高光谱数据维数降低了90%时,9类地物分类实验的总体分类精度可以达到80.2%。     

一种结合波段分组特征和形态学特征的高光谱图像分类方法

如何准确识别图像中的类别信息,是计算机视觉和模式识别领域的重要研究问题。遥感卫星图像数据,尤其是高光谱等遥感图像数据的出现,将空间信息与光谱信息集成于同一数据集中,丰富了图像信息来源。如何准确地识别高光谱图像中的地物类别,已经成为了图像处理和模式识别领域的热点问题。面向高光谱图像数据提出了一种基于波段分组特征和形态学特征的高光谱图像分类方法,结合空间和光谱特征提高分类精度。通过真实的高光谱数据实验证明:利用波段分组可以有效地保持光谱特征,降低数据冗余;在波段分组基础上结合形态学特征进行分类,比传统分类方法的分类精度明显提高。     

高光谱遥感影像纹理特征提取的对比分析

地物的"同物异谱"或"异物同谱"问题,使得仅仅依据高光谱影像的光谱信息较难得到理想的分类精度.纹理特征是地物空间分布的重要结构信息,能够一定程度上弥补光谱特征在高光谱遥感影像分类中的不足.纹理特征提取在高光谱遥感影像分类中得到了诸多发展,然而当前的纹理特征方法缺乏较为全面的对比分析.因此,选取旋转不变局部二值模式、简单...     

基于空间模糊纹理光谱的多光谱遥感图像分类方法

为了充分利用各波段的纹理信息，针对遥感图像不同波段之间具有较大相关性的特点，提出了一种用空间模糊纹理光谱描述多光谱遥感图像纹理特征的方法。根据纹理特征具有多尺度的特性，对原始图像进行二次模糊纹理滤波，一次滤波采用平面三角隶属度函数，二次滤波采用空阃距离代替平面距离形成滤波隶属度函数，其模糊滤波图像的隶属度分布称之为空间模糊纹理光谱。用FasART神经网络分类验证，实验结果表明，该方法具有较高的分类精度，尤其对纹理特征较为复杂的区域的分类效果更为明显。     

基于边缘保持滤波的高光谱影像光谱-空间联合分类

针对高光谱遥感影像分类过程中,高维数据引起的"维数灾难"以及空间邻域一致性信息没有得到充分利用的问题,提出一种基于边缘保持滤波（Edge-preserving filtering,EPF）的高光谱影像光谱-空间联合分类算法.该算法首先进行波段子集划分和主成分提取,构造新的低维特征集,在保存影像结构信息的前提下降低数据维度;其次利用支持向量机（Support vector machine,SVM）获得低维特征集的初始分类概率图;然后利用原始影像主成分对初始分类概率图进行边缘保持滤波,融合光谱信息和空间信息;最后根据滤波后分类概率图对应像素点值的大小确定每个像素的类别.在Indian Pines和Pavia University两组高光谱数据上进行仿真实验,相同实验条件下,本文算法都获得最高分类精度和最少的时间消耗.仿真结果表明本文算法在高光谱遥感影像分类任务中具有明显的优势.     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光G近红外多光谱波段的基础上,新增加的８个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5％。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光-近红外多光谱波段的基础上,新增加的8个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5%。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

基于CNN和农作物光谱纹理特征进行作物分布制图

以卷积神经网络（Convolutional Neural Network, CNN）为代表的深度学习技术,在农作物遥感分类制图领域具有广阔的应用前景。以多时相Landsat 8 多光谱遥感影像为数据源,搭建CNN模型对农作物进行光谱特征提取与分类,并与支撑向量机（SVM）常规分类方法进行对比。进一步引入影像纹理信息,利用CNN对农作物光谱和纹理特征进行提取,优化作物分布提取结果。实验表明：① 基于光谱特征的农作物分布提取,验证结果对比显示,CNN对应各类别精度、总体精度均优于SVM,其中二者总体精度分别为95.14%和91.77%;② 引入影像纹理信息后,基于光谱和纹理特征的CNN农作物分类总体精度提高至96.43%,Kappa系数0.952,且分类结果的空间分布更为合理,可有效区分花生、道路等精细地物,说明纹理特征可用于识别不同作物。基于光谱和纹理信息的CNN特征提取,可面向种植结构复杂区域实现农作物精准分类与分布制图。     

基于残差3DCNN和三维Gabor滤波器的高光谱图像分类

高光谱图像含有数百个波段,包含丰富的光谱信息,因此被广泛应用于地物分类中,但仍存在 着维数灾难的问题。高光谱图像中同时也含有丰富的纹理信息,有效利用纹理信息能够显著提高分类精度。三 维 Gabor 滤波器不仅能够保留图像丰富的光谱信息,还能提取到图像的纹理特征。为了充分利用高光谱图像的 特征,提出一种基于三维 Gabor 和残差三维卷积神经网络(Res-3DCNN)的分类方法。三维卷积神经网络(3DCNN) 能够直接对三维立方体数据进行处理,提取到深层纹理-光谱信息,然而随着网络层的加深会产生网络退化问 题,因此利用残差思想对 3DCNN 模型进行改进。在 PaviaU,Indian Pines 和 Salinas 3 个公共高光谱图像数据 集上进行实验,分别取得 99.17%,97.40%,98.56%的平均分类精度,结果表明该方法能有效提高高光谱图像 的地物分类精度。     

Hyperspectral data spectrum and texture band selection based on the subspace-rough set method

In order to improve the utilization rate of spectroscopic data and texture information, this study proposes a method for optimal selection of spectrum and texture features based on automatic subspace division and rough set theory. This method takes advantage of rough set reduct ideology in order to realize the reduction of different types of ground object spectral features on the basis of the conventional subspace division method. In using this method, the primary spectral band based on spectral information can be determined. Then, the grey-level co-occurrence matrix method can be used to calculate the texture information of the primary spectral band and determine the reduction and optimization in order to obtain the final band based on the spectrum and texture information. Verification of this method is made by using CASI data of Heihe Region, China, and AVIRIS data of the Indiana Region, USA, and also using Support Vector Machine (SVM) classification of the original spectral, primary spectral, and final bands. The results indicate the following. (1) The method for optimal selection of the critical spectral band and texture band, based on the rough set theory, can efficiently improve the classification accuracy of high-spatial resolution remote-sensing images. However, the effects for the low-spatial resolution images are minimal. (2) For high-spatial-resolution remote-sensing images, such as roads, trenches, buildings, and other types of object with obvious textural features, the addition of image texture information can increase the degree of distinction of these different types and thereby improve the classification accuracy. However, the addition of the textural information for some objects with similar texture features will cause misclassification and reduce the classification accuracy for these types of images. (3) This method can realize the optimal selection of spectrum and texture bands of a hyperspectral image and has a certain universality. Also, the texture information will be richer and this method will be more practical through increasing the spatial resolution of images.     

Using low‐spectral‐resolution images to acquire simulated hyperspectral images

We propose a method to acquire simulated hyperspectral images using low‐spectral‐resolution images. Hyperspectral images provide more spectral information than low‐spectral‐resolution images, because of the additional spectral bands used for data acquisition in hyperspectral imaging. Unfortunately, original hyperspectral images are more expensive and more difficult to acquire. However, some research questions require an abundance of spectral information for ground monitoring, which original hyperspectral images can easily provide. Hence, we need to propose a method to acquire simulated hyperspectral images, when original hyperspectral images are especially necessary. Since low‐spectral‐resolution images are readily available and cheaper, we develop a method to acquire simulated hyperspectral images using low‐spectral‐resolution images. With simulated hyperspectral images, we can acquire more ‘hidden’ information from low‐spectral‐resolution images. Our method uses the principles of pixel‐mixing to understand the compositional relationship of spectrum data to an image pixel, and to simulate radiation transmission processes. To this end, we use previously obtained data (i.e. spectrum library) and the sorting data of objects that are derived from a low‐spectral‐resolution image. Using the simulation of radiation transmission processes and these different data, we acquire simulated hyperspectral images. In addition, previous analyses of simulated remotely sensed images do not use quantitative statistical measures, but use qualitative methods, describing simulated images by sight. Here, we quantitatively assess our simulation by comparing the correlation coefficients of simulated images and real images. Finally, we use simulated hyperspectral images, real Hyperion images, and their corresponding ALI images to generate several classification images. The classification results demonstrate that simulated hyperspectral data contain additional information not available in the multispectral data. We find that our method can acquire simulated hyperspectral images quickly.     

结合超像元和子空间投影支持向量机的高光谱图像分类

目的 高光谱图像包含了丰富的空间、光谱和辐射信息,能够用于精细的地物分类,但是要达到较高的分类精度,需要解决高维数据与有限样本之间存在矛盾的问题,并且降低因噪声和混合像元引起的同物异谱的影响。为有效解决上述问题,提出结合超像元和子空间投影支持向量机的高光谱图像分类方法。方法 首先采用简单线性迭代聚类算法将高光谱图像分割成许多无重叠的同质性区域,将每一个区域作为一个超像元,以超像元作为图像分类的最小单元,利用子空间投影算法对超像元构成的图像进行降维处理,在低维特征空间中执行支持向量机分类。本文高光谱图像空谱综合分类模型,对几何特征空间下的超像元分割与光谱特征空间下的子空间投影支持向量机（SVMsub）,采用分割后进行特征融合的处理方式,将像元级别转换为面向对象的超像元级别,实现高光谱图像空谱综合分类。结果 在AVIRIS（airbone visible/infrared imaging spectrometer）获取的Indian Pines数据和Reflective ROSIS（optics system spectrographic imaging system）传感器获取的University of Pavia数据实验中,子空间投影算法比对应的非子空间投影算法的分类精度高,特别是在样本数较少的情况下,分类效果提升明显;利用马尔可夫随机场或超像元融合空间信息的算法比对应的没有融合空间信息的算法的分类精度高;在两组数据均使用少于1%的训练样本情况下,同时融合了超像元和子空间投影的支持向量机算法在两组实验中分类精度均为最高,整体分类精度高出其他相关算法4%左右。结论 利用超像元处理可以有效融合空间信息,降低同物异谱对分类结果的不利影响;采用子空间投影能够将高光谱数据变换到低维空间中,实现有限训练样本条件下的高精度分类;结合超像元和子空间投影支持向量机的算法能够得到较高的高光谱图像分类精度。     

BP Neural Network Classification of Remote Sensing Images based on DT\|CWT Decomposition

In order to solve the ambiguity and uncertainty of high resolution multi\|spectral remote sensing image classification and to better overcome the influence of noise,a new BPNN（Back Propagation Neural Network）classification method of multi\|spectral image,based on DT\|CWT decomposition,is presented in this paper.First,the NDVI and texture features of the image are extracted to reduce the classification uncertainty caused by the problem of different objects having the same spectrum and the same objects having different spectrum in the image,then,the original spectral band,NDVI and texture features of the image are decomposed by DT\|CWT to extract the Low\|frequency information of the image,as well as to reduce the image noise and the presence of “salt and pepper” in the classification.Finally,the extracted low\|frequency sub\|graphs are input to the BP neural network and classified according to the trained network to obtain the final classification result.The results of the comparison show that the proposed method with less miscellaneous points has stronger regional consistency,higher classification accuracy and better robustness.     

A multiresolution spectral angle‐based hyperspectral classification method

Due to the lack of training samples, hyperspectral classification often adopts the minimum distance classification method based on spectral metrics. This paper proposes a novel multiresolution spectral‐angle‐based hyperspectral classification method, where band subsets will be selected to simultaneously minimize the average within‐class spectral angle and maximize the average between‐class spectral angle. The method adopts a pairwise classification framework (PCF), which decomposes the multiclass problem into two‐class problems. Based on class separability criteria, the original set of bands is recursively decomposed into band subsets for each two‐class problem. Each subset is composed of adjacent bands. Then, the subsets with high separability are selected to generate subangles, which will be combined to measure the similarity. Following the PCF, the outputs of all the two‐class classifiers are combined to obtain the final output. Tested with an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set for a six‐class problem, the results demonstrate that our method outperforms the previous spectral metric‐based classification methods.     

Hyperspectral image classification based on bilateral filter with linear spatial correlation information

Support Vector Machine (SVM) with the margin theory is widely used for the hyperspectral classification. However, the margin model is a single interval and does not represent the complete distribution of hyperspectral image data sets. In addition, the spatial texture information obtained by filtering in recent years has become a hot research topic for improving classification of hyperspectral images, but the spatial correlation information is often lost in the spatial texture information extraction. To solve this problem, this paper proposed an algorithm with large margin distribution machine (LDM) that combined the spatial information obtained by the bilateral filter and linear spatial correlation information (BFLSCI-LDM). First, spatial features were extracted by bilateral filter from hyperspectral image whose dimensionality was reduced by principal component analysis. Next, the linear spatial correlation information was constructed for hyperspectral images. Finally, the spatial information and original spectral information were combined for LDM. The experimental results of actual hyperspectral images indicated that the proposed BFLSCI-LDM method was superior to other classification methods, including the original SVM with the raw spectral features, the dimensionality reduction features, and spatial-spectral information, the method of edge-preserving filter and recursive filter, and the LDM-based method.     

Spatial-Spectral Preprocessing based on Nonnegative Matrix Factorization to Unmix Hyperspectral Data

Non-negative Matrix Factorization (NMF)method of blind spectral unmixing can obtain the spectrum and abundance of the endmember by synchronous optimization,without supervising the selection of endmember.Therefore,NMF has been developed rapidly in the application of hyperspectral unmixing.However,traditional blind spectral unmixing NMF method tends to fall into the local optimum and it is difficult to obtain a stable optimal solution.In this paper,we propose an improved Non-negative Matrix Factorization (NMF)method based on Spatial\|Spectal Preprocessing for spectral unmixing of hyperspectral data (SSPP-NMF).First,the SSPP algorithm is used to combine spatial and spectral information to select reasonable and effective dataset.Then,the NMF algorithm is used to unmix this dataset to obtain the final optimized endmember spectrum.Finally,the Non\|Negative Least Squares (NNLS)method is used to obtain the final abundance of the whole study area.The validity and applicability of the proposed method were analyzed based on a set of synthetic hyperspectral data and real hyperspectral images;and then the results were compared with that from three algorithms including the existing NMF algorithm,MVC\|NMF algorithm and ATGP-NMF algorithm.Results show that compared with ATGP-NMF and MVC-NMF,the SSPP algorithm can effectively suppress the influence of noise,significantly improve the performance of the NMF method of blind spectral unmixing algorithm.     

联合快舟一号影像纹理信息的城市土地覆盖分类

仅依靠光谱信息无法满足高分辨率遥感分类的应用需求,辅之以纹理特征信息进行分类,可提高影像分类精度。利用KZ\|1卫星影像和Landsat\|8卫星影像数据,基于面向对象的影像分割法和灰度共生矩阵纹理分析法对新疆石河子市局部城区进行了地表覆盖分类实验,将不同空间分辨率的全色影像纹理信息、光谱信息构成多种影像特征组合进行分类比较研究,以选择最佳的分类特征集。结果表明:KZ-1影像能为城市区域的土地覆盖分类提供丰富的纹理信息,面向对象的影像分割可较好地利用高分辨率数据的几何结构信息实现优化的影像分割,从而提高多光谱影像的分类精度,总体分类精度为90.06%,Kappa系数为87.93%,比单纯利用光谱信息分类的总体精度提高了8.02%,Kappa系数提高了9.65%,表明KZ\|1数据可为光谱分类提供丰富的纹理信息,从而提高城市区域的土地覆盖分类精度。     

以地物识别和分类为目标的高光谱数据挖掘

高光谱信息挖掘技术是高光谱数据应用延拓与深入的重要环节,其核心在于光谱信息的挖掘,基于高光谱遥感信息的特点,探讨分析以地物识别与分类目标的高光谱数据挖掘技术,包括基于模式识别的高光说诺于光谱波形特征的挖掘技术,以及亚象元光谱挖掘。