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。     

基于窗口傅立叶变换功率谱分析的盐田地区高分辨率遥感影像分割分类方法探讨

图像分割是面向对象图像分析的基础。目前常规的图像分割算法普遍基于光谱同质性假设,但是这种假设对于提取干旱地区盐田这种具有析出结晶盐与卤水两种高反差地物共存的空间对象而言显得不足为用。针对面向对象图像分析中只采用光谱和形状异质进行图像分割的不足,以吉兰泰盐田及周边地区2008年11月SPOT 5影像为例,首先采用窗口傅立叶变换功率谱方法提取影像纹理特征,然后进行基于纹理、光谱的多尺度分割,进而对分割后图像进行多层次分类来提取盐田信息。实验结果表明,该方法对盐田地区的信息提取有较好的效果。     

基于子空间-粗集法的高光谱数据光谱与纹理特征优选

为提高光谱数据光谱信息和纹理信息利用率,提出基于自动子空间划分和粗集理论的光谱与纹理特征优选方法。该方法在传统子空间划分法的基础上,利用粗集约简思想对不同类别地物光谱特征进行约简,得到基于光谱的初选波段,再利用灰度共生矩阵法计算出初选光谱波段的纹理信息,并约简优选,得到基于光谱和纹理信息的终选波段。利用黑河生态水文遥感试验中所获取的机载高光谱数据CASI,开展该方法的实证研究。对原始光谱波段、初选光谱波段和终选波段进行SVM(Support Vector Machine)分类,结果表明:与原始光谱数据相比,经过光谱初选得到的初选波段和增加纹理优选的终选波段,总体分类精度分别提高了0.84%和2.78%,Kappa系数分别提高了0.01和0.035;对地物纹理信息进行深度挖掘可以进一步提高遥感影像分类精度。     

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。     

基于独立分量分析的高光谱遥感影像决策树分类

为解决高光谱遥感影像波段众多所带来的信息丰富与“维数灾难”间的矛盾并提高分类精度,针对传统特征选择方法信息损失大的缺陷,基于EO-1 Hyperion高光谱遥感影像,采用独立分量分析(ICA)和决策树分类(DTC)方法联合运作流程,开展影像的地物分类实验研究,提出了ICA-DTC模型。首先运用ICA方法对影像进行特征提取,并以所提取的独立分量特征及其他地理辅助要素组成分类指标集;继而选择适当的指标组合和阈值设定判别规则,建立DTC模型进行影像的地物分类;最后将分类结果与传统最大似然分类法进行比对。结果显示：从分类的总体精度看,前者可达89.34%,高出后者18.8%;从单一地物的分类精度看,前者仅水体的精度略低于后者,而其他11种地物的精度都高于后者。理论分析与实验结果均表明,ICA-DTC模型可有效提高复杂地形条件下的地物分类精度。     

结合KPCA和多尺度纹理的IKONOS遥感影像决策树分类

城市地物类型多样,空间分布复杂,具有很强的非线性特征。核主成分分析(KPCA)通过将特征空间映射到高维核空间,可以表达图像像素间的高阶关系,因而可以提取图像的非线性特征,同时提供一组相互独立的主成分。本文在加入多尺度纹理特征的基础上,以应用地物分布的空间细节信息;且利用核主成分分析(KP-CA)方法对光谱和纹理量提取非线性特征信息,增大类别之间的可分性;并结合决策树分类方法对IKONOS遥感影像分类。实验结果表明:KPCA能很好提取地物之间的非线性特征,结合KPCA和多尺度纹理的决策树分类方法能有效地提取地物类型,提取精度为79.3%,KAPPA系数为0.763.     

利用多属性剖面与双边滤波的高光谱影像分类

随着高光谱影像空间分辨率的提高,"同谱异物"和"同谱异物"问题日益凸显,传统基于纯光谱的地物分类方法已不能满足识别精度要求。结合光谱与空间信息进行高光谱影像分类已成为领域研究热点。鉴于此,该文提出了一种基于扩展多属性剖面(Extended Multi-Attribute Profiles,EMAPs)和指引双边滤波的高光谱影像地物分类方法。首先,基于EMAPs提取高光谱影像4种属性的形态学纹理特征,通过级联纹理与光谱特征获得新特征矢量。其次,对获取的新特征进行降维,并采用指引双边滤波器滤除降维影像的噪声,同时保持影像的边缘信息。最后,采用支持向量机实现分类。高光谱影像分类实验表明,所提方法性能优于多种光谱与空间结合的分类方法。     

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。     

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。     

A spectral–textural kernel-based classification method of remotely sensed images

Most studies have been based on the original computation mode of semivariogram and discrete semivariance values. In this paper, a set of texture features are described to improve the accuracy of object-oriented classification in remotely sensed images. So, we proposed a classification method support vector machine (SVM) with spectral information and texture features (ST-SVM), which incorporates texture features in remotely sensed images into SVM. Using kernel methods, the spectral information and texture features are jointly used for the classification by a SVM formulation. Then, the texture features were calculated based on segmented block matrix image objects using the panchromatic band. A comparison of classification results on real-world data sets demonstrates that the texture features in this paper are useful supplement information for the spectral object-oriented classification, and proposed ST-SVM classification accuracy than the traditional SVM method with only spectral information.     

Study of land cover classification based on knowledge rules using high-resolution remote sensing images

This paper deals with the limitations of visual interpretation of high-resolution remote sensing images and of automatic computer classification completely dependent on spectral data. A knowledge-rule method is proposed, based on spectral features, texture features obtained from the gray-level co-occurrence matrix, and shape features. QuickBird remote sensing data were used for an experimental study of land-use classification in the combination zone between urban and suburban areas in Beijing. The results show that the deficiencies of methods where only spectral data are used for classification can be eliminated, the problem of similar spectra in multispectral images can be effectively solved for the classification of ground objects, and relatively high classification accuracy can be reached.     

Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery

Textural features of high-resolution remote sensing imagery are a powerful data source for improving classification accuracy because using only spectral information is not sufficient for the classification of objects with within-field spectral variability. This study presents the methods of using an object-oriented texture analysis algorithm for improving high-resolution remote sensing imagery classification, including wavelet packet transform texture analysis, the grey-level co-occurrence matrix (GLCM) and local spatial statistics. Wavelet packet transform texture analysis, with the method of optimization and selection of wavelet texture for feature extraction, is a good candidate for object-oriented classification. Feature optimization is used to reduce the data dimensions in combinations of textural sub-bands and spectral bands. The result of the classification accuracy assessment indicates the improvement of texture analysis for object-oriented classification in this study. Compared with the traditional method that uses only spectral bands, the combination of GLCM homogeneity and spectral bands increases the overall accuracy from 0.7431 to 0.9192. Furthermore, wavelet packet transform texture analysis is the optimal method, increasing the overall accuracy to 0.9216 using a smaller data dimension. Local spatial statistical measures also increase the classification total accuracy, but only from 0.7431 to 0.8088. This study demonstrates that wavelet packet and statistical textures can be used to improve object-oriented classification; specifically, the texture analysis based on the multiscale wavelet packet transform is optimal for increasing the classification accuracy using a smaller data dimension.     

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。     

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.     

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.     

基于改进决策树分类算法的遥感影像分类研究

针对现有分类器对遥感影像分类结果存不准确的问题,本文提出了一种基于决策树分类器的遥感影像分类方法,该方法以复合决策树Boost Tree思想为基础,首先利用分形理论中的毯模型提取遥感影像的纹理特征,根据遥感影像分类的特点,构造新的单棵决策树生成算法对遥感影像进行分类。以北京市五环内区域为研究区,使用landsat7 ETM数据源,实现了基于分形纹理特征、光谱特征的改进决策树分类。实验结果表明：通过毯模型提取的纹理特征可以很好地表达表面特征,辅以该纹理信息的改进决策树分类精度相比于只用光谱信息进行分类的精度有一定的提高,改善了分类效果。