CN105005789B - A kind of remote sensing images terrain classification method of view-based access control model vocabulary - Google Patents
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Abstract
本发明公开了一种基于视觉词汇的遥感图像地物分类方法,包括如下步骤:首先将所有的遥感图像分为训练集和测试集,以固定的大小对每幅遥感图像裁剪获得初步切片图,提取包含目标的初步切片图;针对包含目标的初步切片图通过高斯模糊与抽样生成多层高斯空间金字塔;对每一层的图像均进行SIFT特征提取与LBP特征提取;对训练集和测试集中所有遥感图像的遥感单词进行聚类,得到多个聚类中心,所有的聚类中心组成遥感词典;设定不同半径值,对每个初步切片图不同半径值内的遥感单词均建立频率直方图:针对训练集中遥感单词的频率直方图使用支持向量机RBF‑SVM进行训练,然后使用训练后的RBF‑SVM对测试集中遥感图像进行地物分类。
The invention discloses a visual vocabulary-based classification method for remote sensing images, comprising the following steps: first, all remote sensing images are divided into a training set and a test set, and each remote sensing image is cut with a fixed size to obtain a preliminary slice map, Extract the preliminary slice image containing the target; generate a multi-layer Gaussian space pyramid through Gaussian blur and sampling for the preliminary slice image containing the target; perform SIFT feature extraction and LBP feature extraction on the image of each layer; The remote sensing words of the remote sensing image are clustered to obtain multiple cluster centers, and all the cluster centers form a remote sensing dictionary; different radius values are set, and frequency histograms are established for the remote sensing words within different radius values of each preliminary slice image: The frequency histogram of the remote sensing words in the training set is trained using the support vector machine RBF-SVM, and then the trained RBF-SVM is used to classify the remote sensing images in the test set.
Description
技术领域technical field
本发明属于遥感图像处理技术领域,涉及一种基于高分辨率遥感图像的地物分类方法。The invention belongs to the technical field of remote sensing image processing, and relates to a ground object classification method based on high-resolution remote sensing images.
背景技术Background technique
随着遥感卫星的迅速发展,获得的遥感卫星影像的分辨率也越来越高,由此引发的对于遥感图像的处理要求也越来越高,其中,对于高分辨率遥感卫星的目标物分类识别是一个重要的研究方向,尤其是城市配置科学性,农业种植分布规划以及军事敏感目标分类提取均有重要意义。同时高分辨率遥感卫星的高分辨率也意味着细节更丰富,背景对目标的影响也越大,这就为图像的分类带来了挑战。With the rapid development of remote sensing satellites, the resolution of obtained remote sensing satellite images is getting higher and higher, and the resulting processing requirements for remote sensing images are also getting higher and higher. Among them, the object classification of high-resolution remote sensing satellites Identification is an important research direction, especially the scientificity of urban configuration, the distribution planning of agricultural planting and the classification and extraction of military sensitive targets are all of great significance. At the same time, the high resolution of high-resolution remote sensing satellites also means that the details are richer, and the background has a greater influence on the target, which brings challenges to image classification.
在遥感图像分类领域里,主要包括两类分类方法:一类是非监督的方法,通过使用聚类的思想来实现,包括k-means,isodata等方法。但是随着遥感图像分辨率越来越高会有类间方差减少,类内方差增大,从而导致类间重叠,出现同物异谱以及同谱异物的现象,导致分类错误;另一类是有监督方法,也是现在普遍使用的分类方法,采用学习与训练的思想,包括BP神经网络模型,遗传模型及支持向量机等,监督的方法具备一定的增量调整性,但是当遥感图像分辨率不断增高时,对于训练样本的选择以及对于每类样本的训练速度都是一个挑战,尤其是训练样本的选择是一个人工选择的过程,带来更大的时间成本。In the field of remote sensing image classification, there are mainly two types of classification methods: one is unsupervised method, which is realized by using the idea of clustering, including k-means, isodata and other methods. However, as the resolution of remote sensing images becomes higher and higher, the variance between classes will decrease, and the variance within classes will increase, resulting in overlapping between classes, the phenomenon of the same object with different spectra and the same spectrum with different objects, resulting in classification errors; the other type is The supervised method is also a commonly used classification method now, using the idea of learning and training, including BP neural network model, genetic model and support vector machine, etc. The supervised method has a certain incremental adjustment, but when the remote sensing image resolution When it keeps increasing, it is a challenge for the selection of training samples and the training speed of each type of samples, especially the selection of training samples is a process of manual selection, which brings greater time cost.
同时高分辨率遥感图像的发展意味着特征选择的种类增多,以颜色、纹理、形状等底层特征为基础建立起来的与样本高层信息的关系,会出现泛化性差,适用度不高的缺点。为了克服底层特征与样本高层次语义信息的鸿沟,由此诞生了以样本中间特征为基础的与样本高层次语义信息之间的推导模型。 BOV(视觉词袋)模型作为中间特征的代表,在OBIA(object-based remote-sensing image analysis)方面已经有了一些进展。但是传统的BOV模型方法自身有缺陷,不能够良好的利用图像的全部信息,例如空间层次信息等,并且BOV模型分类主要应用在自然物体的分类识别,对于遥感图像地物分类没有很好利用。At the same time, the development of high-resolution remote sensing images means that the types of feature selection are increasing, and the relationship between the low-level features such as color, texture, and shape and the high-level information of the sample will have the disadvantages of poor generalization and low applicability. In order to overcome the gap between low-level features and high-level semantic information of samples, a derivation model based on intermediate features of samples and high-level semantic information of samples was born. The BOV (Bag of Visual Words) model, as a representative of intermediate features, has made some progress in OBIA (object-based remote-sensing image analysis). However, the traditional BOV model method has its own defects, and cannot make good use of all the information of the image, such as spatial hierarchical information, etc., and the BOV model classification is mainly used in the classification and recognition of natural objects, and it is not well utilized for the classification of remote sensing images.
如何运用词袋的思想来解决遥感图像地物分类已经成了遥感图像领域重要的研究方向之一。但是由于遥感图像地物种类多种多样,单一的特征词袋方法和单一的遥感单词频率直方图很难代表所有的遥感图像种类。How to use the idea of bag of words to solve the classification of remote sensing images has become one of the important research directions in the field of remote sensing images. However, due to the variety of ground objects in remote sensing images, it is difficult to represent all types of remote sensing images with a single bag of feature words method and a single frequency histogram of remote sensing words.
发明内容Contents of the invention
有鉴于此,本发明提供了一种基于视觉词汇的遥感图像地物分类方法,解决了遥感图像地物分类准确率不足的问题。In view of this, the present invention provides a visual vocabulary-based remote sensing image feature classification method, which solves the problem of insufficient classification accuracy of remote sensing image feature.
为了达到上述目的,本发明的技术方案包括如下步骤:In order to achieve the above object, the technical solution of the present invention comprises the following steps:
步骤一、将所有的遥感图像分为训练集和测试集,对于每幅遥感图像,以固定的大小对其裁剪获得初步切片图,提取包含目标的初步切片图。Step 1. Divide all remote sensing images into training set and test set. For each remote sensing image, cut it with a fixed size to obtain a preliminary slice map, and extract the preliminary slice map containing the target.
步骤二、针对步骤一中提取的包含目标的初步切片图通过高斯模糊与抽样生成多层高斯空间金字塔。Step 2: Generate a multi-layer Gaussian space pyramid through Gaussian blurring and sampling for the preliminary slice image containing the target extracted in step 1.
步骤三、对高斯空间金字塔中的每一层的图像均进行特征提取,特征提取包括局部特征即SIFT特征提取与图像纹理特征即LBP特征提取。Step 3, perform feature extraction on the image of each layer in the Gaussian space pyramid, feature extraction includes local features, namely SIFT feature extraction, and image texture features, namely LBP feature extraction.
在进行SIFT特征提取时,只进行一层高斯滤波,然后通过滑窗的方式将图像分为16×16的细分切片图,根据每一个细分切片图的梯度变化进行SIFT特征 向量提取得到关于该细分切片图的128维的SIFT特征向量;在每个细分切片图中根据设定的半径和采样点数确定关于该细分切片图的LBP特征向量。When performing SIFT feature extraction, only one layer of Gaussian filtering is performed, and then the image is divided into 16×16 subdivision slices by means of a sliding window, and the SIFT feature vector is extracted according to the gradient change of each subdivision slice to obtain about The 128-dimensional SIFT feature vector of the subdivision slice map; in each subdivision slice map, determine the LBP feature vector about the subdivision slice map according to the set radius and the number of sampling points.
则每个细分切片图对应的SIFT特征向量和LBP特征向量组合形成一个遥感单词;由此生成关于该遥感图像的遥感单词。Then the SIFT feature vector and the LBP feature vector corresponding to each subdivision slice map are combined to form a remote sensing word; thereby generating a remote sensing word about the remote sensing image.
其中最高层的高斯空间金字塔满足其初步切片图中对应得到遥感单词个数至少为遥感词典的数量的1/2。Among them, the Gaussian spatial pyramid at the highest level satisfies that the number of remote sensing words corresponding to its preliminary slice map is at least 1/2 of the number of remote sensing dictionaries.
针对每一层高斯金字塔中的初步切片图采取如下步骤四~五的方式进行处理:For the preliminary slice image in each layer of Gaussian pyramid, the following steps 4-5 are used for processing:
步骤四、对训练集和测试集中所有遥感图像的遥感单词进行聚类,得到多个聚类中心,所有的聚类中心组成遥感词典。Step 4, clustering the remote sensing words of all the remote sensing images in the training set and the test set to obtain multiple cluster centers, and all the cluster centers form a remote sensing dictionary.
在聚类过程中,对遥感单词采用PCA主成分分析法进行降维,保留对协方差贡献最大的维度,并设定数据丢失率阈值disratio,使得数据丢失率不超过 disratio。In the clustering process, PCA principal component analysis method is used to reduce the dimension of remote sensing words, retain the dimension that contributes the most to the covariance, and set the data loss rate threshold disratio so that the data loss rate does not exceed disratio.
步骤五、设定不同半径值,对每个初步切片图不同半径值内的遥感单词均建立频率直方图:Step five, set different radius values, and establish a frequency histogram for the remote sensing words in different radius values of each preliminary slice map:
该直方图的横坐标为每个遥感单词,纵坐标为遥感单词在当前半径值内的初步切片图中出现的频率,该频率的计算方法为:频率值初始为0,计算当前半径值内的初步切片图中两两遥感单词之间的欧氏距离,若当前遥感单词A到另一遥感单词B之间的欧式距离为其他所有遥感单词到B的欧式距离的最小值,则A的频率值增加1。The abscissa of the histogram is each remote sensing word, and the ordinate is the frequency of remote sensing words appearing in the preliminary slice map within the current radius value. The Euclidean distance between two remote sensing words in the preliminary slice image, if the Euclidean distance between the current remote sensing word A and another remote sensing word B is the minimum value of the Euclidean distance between all other remote sensing words and B, then the frequency value of A increase by 1.
步骤六、针对训练集中遥感单词的频率直方图使用支持向量机RBF-SVM进行训练,然后使用训练后的RBF-SVM对测试集中遥感图像进行地物分类。Step 6: Use the support vector machine RBF-SVM to train the frequency histogram of the remote sensing words in the training set, and then use the trained RBF-SVM to classify the remote sensing images in the test set.
若遥感单词所属的高斯金字塔的层数在最底层,则分配其对应的支持向量机中分类器的权值最大,层数越往上,对应的分类器的权值越小。If the number of layers of the Gaussian pyramid to which the remote sensing word belongs is at the bottom, the weight of the classifier in the corresponding support vector machine is assigned to be the largest, and the higher the number of layers, the smaller the weight of the corresponding classifier.
进一步地,通过滑窗的方式将图像分为16*16的细分切片图时,滑窗的步长设置为1到8之间。Further, when the image is divided into 16*16 subdivision slices by means of a sliding window, the step size of the sliding window is set between 1 and 8.
进一步地,RBF-SVM支持向量机中的核函数选择histogram intersectionkernel。Further, the kernel function in the RBF-SVM support vector machine selects histogram intersectionkernel.
进一步地,步骤二中,聚类时采用K-means聚类方法。Further, in step 2, the K-means clustering method is used for clustering.
有益效果:Beneficial effect:
1、该方法首先将遥感图像集分为测试集合训练集两部分,然后对所有的遥感图像切片进行特征点提取,得到每幅切片图的单词,之后针对训练集的特征点进行聚类得到词典,最后针对于词典和测试集的单词构建频率直方图进行训练,使用测试集的单词直方图进行测试,能够更加准确的对遥感图片地物进行分类,分类结果更加准确。1. This method first divides the remote sensing image set into two parts, the test set and the training set, and then extracts feature points from all remote sensing image slices to obtain the words of each slice, and then clusters the feature points of the training set to obtain a dictionary , and finally build a frequency histogram for the words in the dictionary and the test set for training, and use the word histogram of the test set for testing, which can more accurately classify remote sensing images and features, and the classification results are more accurate.
2、本方法中在对切片图进行高斯模糊和抽样时采用多层高斯金字塔的方法,因此能够更好地利用遥感图像的空间信息,从而使得分类更加准确。2. In this method, a multi-layer Gaussian pyramid method is used when performing Gaussian blur and sampling on the sliced image, so the spatial information of the remote sensing image can be better utilized, thereby making the classification more accurate.
3、本方法在传统的由于SIFT特征向量只包含16*16切片图中的方向和梯度信息,即切片图中稳定点的信息,不具备整个切片图的纹理特征信息,因此在SIFT特征的基础上加入旋转不变均匀LBP特征,弥补了SIFT特征中不具备的纹理特征信息,能够全方位地表达遥感图像中的所有信息。3. In the traditional method, because the SIFT feature vector only contains the direction and gradient information in the 16*16 slice map, that is, the information of the stable point in the slice map, it does not have the texture feature information of the entire slice map. Therefore, based on the SIFT feature The rotation-invariant uniform LBP feature is added to it, which makes up for the texture feature information that does not exist in the SIFT feature, and can express all the information in the remote sensing image in an all-round way.
4、本方法中在生成频率直方图时,选取多个不同的半径,同时保证所选的半径应当使得内部包含足够的遥感单词数量,对金字塔内每一层切片图不同半径内的遥感单词与遥感词典生成频率直方图,这样能够在满足生成遥感单词频率直方图的需求的基础上,同时利用遥感图像中从中心处到周围的空间信息,从而能够更好地表达遥感图像中的目标,提高分类准确度。4. In this method, when generating the frequency histogram, select a plurality of different radii, and ensure that the selected radius should make the interior contain enough remote sensing words, and the remote sensing words and The frequency histogram generated by the remote sensing dictionary can meet the needs of generating the frequency histogram of remote sensing words, and at the same time use the spatial information from the center to the surrounding in the remote sensing image, so that the target in the remote sensing image can be better expressed and improved. classification accuracy.
5、本方法最终使用多分类器加决策器得到决策结果,针对高斯空金字塔中每一层的遥感单词频率直方图进行分类,对不同的分类器分配权值,我们认为高斯金字塔底层的信息权值最大,越往上信息权值越小,经过决策得到遥感切片图的类属,通过给每一次高斯金字塔分类结果分配权值,不单纯信任某一层图像的分类结果,通过不同层次高斯金字塔的分类结果共同确定遥感图像中的目标,提高分类准确度。5. This method finally uses a multi-classifier plus a decision maker to obtain the decision result, classifies the frequency histogram of remote sensing words at each layer in the Gaussian empty pyramid, and assigns weights to different classifiers. We believe that the information weight at the bottom of the Gaussian pyramid The value is the largest, and the higher the information weight is, the smaller the information weight is. After decision-making, the category of the remote sensing slice map is obtained. By assigning weights to each Gaussian pyramid classification result, not simply trusting the classification results of a certain layer of images, through different levels of Gaussian pyramids. The classification results jointly determine the target in the remote sensing image and improve the classification accuracy.
附图说明Description of drawings
图1为本发明的实施流程图。Fig. 1 is the implementation flowchart of the present invention.
图2为本发明遥感单词的生成流程图。Fig. 2 is the generation flowchart of the remote sensing word of the present invention.
图3为本发明遥感单词频率直方图的生成流程图。Fig. 3 is a flow chart for generating a remote sensing word frequency histogram in the present invention.
图4为本发明分类器的设计。Fig. 4 is the design of the classifier of the present invention.
具体实施方式Detailed ways
下面结合附图并举实施例,对本发明进行详细描述。The present invention will be described in detail below with reference to the accompanying drawings and examples.
步骤一、将所有的遥感图像分为训练集和测试集,对于每幅遥感图像,以固定的大小对其裁剪获得初步切片图,提取包含目标的初步切片图;Step 1. Divide all remote sensing images into a training set and a test set. For each remote sensing image, cut it with a fixed size to obtain a preliminary slice map, and extract the preliminary slice map containing the target;
步骤二、针对步骤一种提取的包含目标的初步切片图通过高斯模糊与抽样生成多层高斯空间金字塔,高斯空间金字塔的层数依据初步切片图的尺寸确定;Step 2. Generate a multi-layer Gaussian space pyramid through Gaussian blurring and sampling for the preliminary slice image containing the target extracted in step 1. The number of layers of the Gaussian space pyramid is determined according to the size of the preliminary slice image;
附图2为遥感单词的生成流程图,包含高斯空间金字塔生成及多特征提取两部分。Accompanying drawing 2 is the flow chart of generation of remote sensing words, including two parts of Gaussian space pyramid generation and multi-feature extraction.
首先针对遥感切片图通过高斯模糊与抽样生成多层高斯空间金字塔,针对于不同尺寸的切片图,可以生成不同层数的高斯空间金字塔,最高层的金字塔要求能够满足至少得到遥感词典数量的1/2以构成之后的遥感单词频率直方图,本实施例中为16组特征值,这样能够保证所生成的频率直方图有意义。Firstly, multi-layer Gaussian space pyramids are generated by Gaussian blur and sampling for remote sensing slices. For slices of different sizes, Gaussian space pyramids with different layers can be generated. The pyramid at the highest level can meet at least 1/1 of the number of remote sensing dictionaries. 2. To form the remote sensing word frequency histogram, in this embodiment, there are 16 groups of eigenvalues, which can ensure that the generated frequency histogram is meaningful.
多层的高斯空间金字塔能够更好地利用遥感图像的空间信息,从而使得分类更加准确。The multi-layer Gaussian spatial pyramid can make better use of the spatial information of remote sensing images, thus making the classification more accurate.
其次,针对高斯空间金字塔中的每一幅图像进行多特征(SIFT特征与LBP 特征)提取。为了保持每幅图像频率直方图数量的一致性,对SIFT特征进行改变,只进行一层高斯滤波,通过滑窗的方式将图像分为细分切片图,本实施例中为16*16的切片图,滑窗的步长可以根据实际需要设置为1到8,根据每一个 16*16的细分切片图的梯度变化得到128维的SIFT特征向量。这样针对于每一副图像的SIFT特征点数一致,便于之后的直方图统计。Secondly, multi-feature (SIFT feature and LBP feature) extraction is performed for each image in the Gaussian space pyramid. In order to maintain the consistency of the number of frequency histograms of each image, the SIFT feature is changed, only one layer of Gaussian filtering is performed, and the image is divided into subdivided slices by means of a sliding window. In this embodiment, it is a slice of 16*16 In the figure, the step size of the sliding window can be set from 1 to 8 according to actual needs, and a 128-dimensional SIFT feature vector is obtained according to the gradient change of each 16*16 subdivision slice map. In this way, the number of SIFT feature points for each image is consistent, which is convenient for subsequent histogram statistics.
由于SIFT特征向量只包含16*16切片图中的方向和梯度信息,即切片图中稳定点的信息,不具备整个切片图的纹理特征信息,因此在SIFT特征的基础上加入旋转不变均匀LBP特征以弥补对于图像的表征,能够全方位地表达遥感图像中的所有信息。在每个16*16的切片图中根据半径和采样点数(人为设定) 确定LBP特征,由于采用了旋转不变的LBP特征,使得代表图像纹理特征的维数较少。Since the SIFT feature vector only contains the direction and gradient information in the 16*16 slice map, that is, the information of the stable point in the slice map, it does not have the texture feature information of the entire slice map, so the rotation invariant uniform LBP is added to the SIFT feature Features can make up for the representation of images, and can express all the information in remote sensing images in an all-round way. In each 16*16 slice image, the LBP feature is determined according to the radius and the number of sampling points (manually set). Since the rotation-invariant LBP feature is used, the dimensionality representing the image texture feature is less.
将每幅图像的SIFT特征与旋转均匀不变LBP特征结合起来就是代表这幅图像的遥感单词(每个16*16的切片图对应一个遥感单词),从每幅切片图生成的遥感单词数量是一致的。Combining the SIFT feature of each image with the LBP feature of uniform rotation is the remote sensing word representing this image (each 16*16 slice map corresponds to a remote sensing word), and the number of remote sensing words generated from each slice map is consistent.
步骤二、基于训练集和测试集中所有的遥感单词进行聚类,得到多个聚类中心,所有的聚类中心就组成了遥感词典。Step 2: Clustering is performed based on all remote sensing words in the training set and the test set to obtain multiple cluster centers, and all the cluster centers form a remote sensing dictionary.
由于聚类是一个离线的过程,随着聚类数据的增多,通常意义的整体划分聚类方法会产生几何倍数的运算代价。因此降维聚类的思想来处理大规模以及超大规模的遥感词典数据。Since clustering is an offline process, with the increase of clustering data, the general sense of the overall division clustering method will produce geometric multiples of the calculation cost. Therefore, the idea of dimensionality reduction clustering is used to deal with large-scale and ultra-large-scale remote sensing dictionary data.
在这里,对遥感单词采用PCA主成分分析法进行降维,保留对协方差贡献最大的维度,但是需要注意的是,降维个数需要保证不大量丢失有用数据,因此,定义数据丢失率disratio,保证余下数据保留大部分有用信息。Here, the PCA principal component analysis method is used to reduce the dimensionality of the remote sensing words, and the dimension that contributes the most to the covariance is retained. However, it should be noted that the number of dimensionality reduction needs to ensure that a large amount of useful data is not lost. Therefore, the definition of the data loss rate disratio , ensuring that the rest of the data retains most of the useful information.
聚类的方法采用时间效率和空间效率均衡的K-means聚类方法,K-means 聚类可以直接得到聚类中心,并且聚类中心是类内所有属性的平均值,可以很好的代表类内中心。聚类中心的个数需要根据分类的个数确定,需要分类的类别个数越多,需要的聚类中心就越多,也就意味着遥感词典中的单词数越多,才能满足多类目标分类准确率的需求。The clustering method adopts the K-means clustering method with balanced time efficiency and space efficiency. K-means clustering can directly obtain the cluster center, and the cluster center is the average value of all attributes in the class, which can well represent the class inner center. The number of cluster centers needs to be determined according to the number of classifications. The more categories that need to be classified, the more cluster centers are needed, which means that the more words in the remote sensing dictionary can meet the multi-category goals. classification accuracy requirements.
步骤三、设定不同半径值,对每个初步切片图不同半径值内的遥感单词均建立频率直方图:Step 3, set different radius values, and establish a frequency histogram for the remote sensing words in different radius values of each preliminary slice map:
该直方图的横坐标为每个遥感单词,纵坐标为遥感单词在当前半径值内的初步切片图中出现的频率,该频率的计算方法为:频率值初始为0,计算当前半径值内的初步切片图中两两遥感单词之间的欧氏距离,若当前遥感单词A到另一遥感单词B之间的欧式距离为其他所有遥感单词到B的欧式距离的最小值,则A的频率值增加1。The abscissa of the histogram is each remote sensing word, and the ordinate is the frequency of remote sensing words appearing in the preliminary slice map within the current radius value. The Euclidean distance between two remote sensing words in the preliminary slice image, if the Euclidean distance between the current remote sensing word A and another remote sensing word B is the minimum value of the Euclidean distance between all other remote sensing words and B, then the frequency value of A increase by 1.
附图3为本发明遥感单词频率直方图的生成流程图,在这里为了有效利用图片中的空间位置信息,针对于不同层次的空间金字塔,选取合适的半径,对金字塔内每一层切片图不同半径内的遥感单词与遥感词典生成频率直方图,将关于这一切片图所有的遥感单词频率直方图组合,作为代表这一初步切片图的遥感单词频率直方图。Accompanying drawing 3 is the generation flow diagram of remote sensing word frequency histogram of the present invention, here in order to effectively utilize the space position information in the picture, for the space pyramid of different levels, choose suitable radius, to each layer slice figure in the pyramid is different The remote sensing words within the radius and the remote sensing dictionary generate a frequency histogram, and combine all the remote sensing word frequency histograms on this slice to represent the remote sensing word frequency histogram of this preliminary slice.
选取合适的半径时,应当满足其内部包含足够的遥感单词数量,从而满足生成遥感单词频率直方图的需求。选择多个半径的好处是利用遥感图像中从中心处到周围的空间信息,从而能够更好地表达遥感图像中的目标,提高分类准确度。When selecting an appropriate radius, it should satisfy the requirement that it contains enough remote sensing words to meet the requirement of generating the frequency histogram of remote sensing words. The advantage of choosing multiple radii is to use the spatial information from the center to the surrounding in the remote sensing image, so that the target in the remote sensing image can be better expressed and the classification accuracy can be improved.
步骤四、针对训练集的遥感单词频率直方图和测试集的遥感频率直方图使用RBF-SVM支持向量机进行训练,核函数选择在统计领域有良好表现的 histogram intersectionkernel。Step 4: Use the RBF-SVM support vector machine for training on the remote sensing word frequency histogram of the training set and the remote sensing frequency histogram of the test set, and select the histogram intersection kernel with good performance in the statistical field as a kernel function.
附图4为分类器的构成,使用多分类器加决策器得到决策结果,针对高斯空金字塔中每一层的遥感单词频率直方图进行分类,对不同的分类器分配权值,我们认为高斯金字塔底层的信息权值最大,越往上信息权值越小,经过决策得到遥感切片图的类属。通过给每一次高斯金字塔分类结果分配权值,不单纯信任某一层图像的分类结果,通过不同层次高斯金字塔的分类结果共同确定遥感图像中的目标,提高分类准确度。Attached Figure 4 shows the composition of the classifier. The decision result is obtained by using multiple classifiers plus a decision maker. The frequency histogram of remote sensing words in each layer of the Gaussian empty pyramid is classified, and the weights are assigned to different classifiers. We think that the Gaussian pyramid The information weight at the bottom layer is the largest, and the information weight is smaller as you go up, and the category of the remote sensing slice map is obtained through decision-making. By assigning weights to each Gaussian pyramid classification result, we do not simply trust the classification results of a certain layer of images, but use the classification results of different levels of Gaussian pyramids to jointly determine the target in the remote sensing image and improve the classification accuracy.
自此,就完成了遥感图像的地物分类。Since then, the object classification of remote sensing images has been completed.
综上,以上仅为本发明的较佳实施例而已,并非用于限定本发明的保护范围。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。To sum up, the above are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.
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