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。     

一种基于植被指数的遥感影像决策树分类方法

以江苏省徐州市为研究区,采用2000年ETM+多光谱影像作为遥感信息源,选择影像的光谱特征和归一化植被指数（NDVI）、绿度植被指数（GVI）、比值植被指数（RVI）等10种植被指数作为分类特征,基于See5决策树学习软件构建分类决策树,实现了研究区景观格局的遥感分类。研究结果表明,决策树分类法易于综合多种特征进行遥感影像的分类,植被指数参与到决策树分类中能够提高分类的总体精度。     

基于模糊高斯基函数神经网络的遥感图像分类

针对遥感图像分类的特点,提出了一种基于模糊高斯基函数神经网络的遥感图像分类器。该分类器将模糊技术与神经网络相结合,采用神经网络来实现模糊推理,利用神经网络的学习能力来达到调整模糊隶属函数和模型规则的目的,从而使系统具备了自适应的特性,实验结果表明,这种基于模糊高斯基孙数神经网络的分类器经过训练后,可应用于遥感图像的分类,其分类精度明显高于传统的最大似然分类法。     

遥感影像亚像元制图方法研究进展综述

遥感影像混合像元的普遍存在给遥感影像解译造成困扰。有效处理混合像元问题,细化分类结果,获得更为精细的地物细节信息就需要进行亚像元绘图。目前亚像元制图方法主要包括3个步骤:① 混合像元分解;② 提取软信息;③ 亚像元制图。总结归纳了近年来遥感影像亚像元绘图领域的研究进展和成果,详细阐述了亚像元制图的步骤及涉及的研究方法。依据辅助信息的类型将亚像元绘图方法大致划分为:基于空间相关性、基于空间结构信息、基于神经网络、基于像元交换途径的4类亚像元分类方法,并分别对各种方法的优缺点进行了分析对比。最后,评述了亚像元制图的发展趋势。     

利用混合像元分解结合SVM提取城市绿地

从遥感图像提取城市绿地是准确获取城市绿地空间分布的基础。然而由于混合像元的存在,导致城市遥感分类精度不高。因此,利用混合像元分解结合SVM（支持向量机）法提取北京市TM图像城市绿地,并与决策树法比较,研究提高遥感提取城市绿地精度的方法。结果表明,该方法较适合复杂高维空间,对样本选取的准确性没有那么苛刻,可有效地处理城市遥感图像存在的混合像元问题,可较准确地提取城市绿地信息,其精度在92%以上,优于决策树法。     

MESMA与面向对象组合的土地利用分类方法

混合像元是制约传统组合分类方法精度提高的主要因素之一.为此,文章提出一种基于多端元混合像元分解(multiple endmember spectral mixture analysis,MESMA)与面向对象分类组合的分类方法,利用混合像元分解提高分类精度,借助组合方法降低"椒盐"现象影响.首先,使用M ESM A技术...     

Possibilities and limitations of artificial neural networks for subpixel mapping of land cover

Although developments in remote sensing have greatly improved land cover mapping, the mixed pixel problem has not yet been fully addressed. Soft classification techniques have been introduced to address the problem, but they do not show the spatial location of the class proportions in a pixel. Subpixel mapping has been introduced to address the drawbacks of soft classifications. In this work, the feedforward backpropagating neural network (FFBPNN) was used for subpixel mapping. A set of class proportion images, which are to be treated as soft classification results, were created from a high spatial resolution (25 m) land cover dataset. For this purpose, the land cover dataset was aggregated both thematically (into two, four or eight land cover classes) and spatially (into proportion images with pixel sizes of 75, 150 and 300 m). This resulted in nine different combinations that were considered here as study cases. Several FFBPNNs were trained using these proportion images and the original land cover dataset (which was used as a target). Subsequently, the best networks were used to reconstruct high spatial resolution land cover maps of two heterogeneous areas in the south of The Netherlands. The overall accuracies obtained revealed that the networks were influenced by the spatial frequency, shape and size of the different land cover types. Moreover, it was revealed that most of the errors were on the class boundaries where highly mixed pixels are to be expected. The accuracies spanned a wide range of values depending on the complexity of the cases. Although it was not possible to exhaustively explore all network architectures, the results demonstrate the potential of the FFBPNN for subpixel mapping.     

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the ‘hard’ classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.     

Early Estimation of Winter Wheat Planting Area in Qingyang City by Decision Tree and Pixel UnmixingMethods based on GF-1 Satellite Data

In Northwest China,there are many mixed pixels in the winter wheat area,so the combination of decision tree and mixed pixel decomposition is of great significance to improve the interpretation accuracy.The data source of this result is GF-1 satellite data which excellent in the high temporal resolution and high spatial resolution.Based on the difference about variation characteristics and NDVI value for winter wheat and the other crops in different phase data,we build decision tree to extract winter wheat pixels preliminary.Then selected linear spectral mixture model,further analysis the previous data by mixed pixel decomposition,get the final planting area data more exactly.Compared with the winter wheat samples measurement data,calculate the extraction accuracy eventually.The result shows that the extraction accuracy of winter wheat planting area in the study area was more than 90%,Kappa coefficient is close to 0.8,can reflect the distribution of winter wheat in the region accurately.This study found that the method which combined with decision tree classification and pixel unmixing based on high resolution remote sensing image can extract the winter wheat planting area precisely,This is helpful for the development of crop area remote sensing monitoring.     

一种基于模糊关联分类的遥感图像分类方法

遥感图像分类是遥感领域的研究热点之一.提出了一种基于自适应区间划分的模糊关联遥感图像分类方法(fuzzy associative remote sensing classification,FARSC).算法根据遥感图像分类的特点,利用模糊C均值聚类算法自适应地建立连续型属性模糊区间,使用新的剪枝策略对项集进行筛选从而避免生成无用规则,采用一种新的规则重要性度量方法对多模糊分类规则进行融合,从而有效地提高分类效率和精确度.在UCI数据和遥感图像上所作实验结果表明,算法具有较高的分类精度以及对样本数量变化的不敏感性,对于解决遥感图像分类问题,FARSC算法具有较高的实用性,是一种有效的遥感图像分类方法.     

SVM结合模糊方法在遥感图像分类中的应用

提出一种支持向量机(SVM)结合模糊方法的遥感图像分类算法。首先介绍了SVM基本算法及其在遥感图像分类中应用情况,然后针对SVM多类判别存在混分和漏分的缺陷,对混分和漏分样本采用模糊方法判决分类。实验证明该方法的分类精度优于单一的SVM方法、模糊方法或神经网络方法。     

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。     

基于概率扩散的多光谱遥感图像分类模型

为了提高遥感图像分类精度,提出了一种基于概率扩散模型的多光谱遥感图像自动分类技术。该方法首先通过比较模糊C均值分类器（FCM）的有效性函数来自动确定最优分类数目,然后利用基于形态学的各向异性概率扩散模型来调整中心像元隶属类别的概率,最后根据概率扩散的隶属概率向量图,并按照最大后验概率估计（MAP）对像元进行分类。由于各向异性扩散具有保边缘平滑的特点,因此,该概率扩散模型不仅能够有效地抑制同质区域内部“斑点”的产生。而且使得图像上重要的边缘特征得到了较好地保留。实验结果表明,该分类算法不仅能够避免分类图像中“斑点”噪声的影响,而且分类后的总体精度达到了77．76％和Kappa系数达到了0．7198,均优于未经过概率扩散的最大后验概率估计分类算法,因而具有一定的实用价值。     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

基于元胞自动机模型的遥感图像亚像元定位

由于遥感图像中普遍存在混合像元,因此传统分类方法得到的结果通常会存在较大误差,应用混合像元分解技术,虽然可以得到混合像元中各端元组分的丰度,但是却不能得到各端元组分的空间分布状态,而亚像元定位则是在混合像元分解的基础上,将混合像元剖分为亚像元,再利用端元组分的丰度及像元空间分布的特点,将亚像元赋予不同端元组分来得到各端元组分的空间分布情况,以提高遥感图像分类的精度。为了更好地解决亚像元定位问题,结合亚像元定位的理论模型,提出了一种新的元胞自动机模型,并通过模拟数据和实际数据对该模型进行了检验,结果表明,该模型是一种简单有效的解决亚像元定位问题的方法。     

Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images

Detecting change areas among two or more remote sensing images is a key technique in remote sensing. It usually consists of generating and analyzing a difference image thus to produce a change map. Analyzing the difference image to obtain the change map is essentially a binary classification problem, and can be solved by optimization algorithms. This paper proposes an accelerated genetic algorithm based on search-space decomposition (SD-aGA) for change detection in remote sensing images. Firstly, the BM3D algorithm is used to preprocess the remote sensing image to enhance useful information and suppress noises. The difference image is then obtained using the logarithmic ratio method. Secondly, after saliency detection, fuzzy c-means algorithm is conducted on the salient region detected in the difference image to identify the changed, unchanged and undetermined pixels. Only those undetermined pixels are considered by the optimization algorithm, which reduces the search space significantly. Inspired by the idea of the divide-and-conquer strategy, the difference image is decomposed into sub-blocks with a method similar to down-sampling, where only those undetermined pixels are analyzed and optimized by SD-aGA in parallel. The category labels of the undetermined pixels in each sub-block are optimized according to an improved objective function with neighborhood information. Finally the decision results of the category labels of all the pixels in the sub-blocks are remapped to their original positions in the difference image and then merged globally. Decision fusion is conducted on each pixel based on the decision results in the local neighborhood to produce the final change map. The proposed method is tested on six diverse remote sensing image benchmark datasets and compared against six state-of-the-art methods. Segmentations on the synthetic image and natural image corrupted by different noise are also carried out for comparison. Results demonstrate the excellent performance of the proposed SD-aGA on handling noises and detecting the changed areas accurately. In particular, compared with the traditional genetic algorithm, SD-aGA can obtain a much higher degree of detection accuracy with much less computational time.     

Mapping Land Cover from Remotely Sensed Data with a Softened Feedforward Neural Network Classification

Remote sensing has considerable potential as a source of data for land cover mapping. This potential remains to be fully realised due, in part, to the methods used to extract land cover information from the remotely sensed data. Widely used statistical classifiers provide a poor representation of land cover, make untenable assumptions about the data and convey no information on the quality of individual class allocations. This paper shows that a softened classification, providing information on the strength of membership to all classes for each image pixel, may be derived from a neural network. This information may be used to indicate classification quality on a per-pixel basis. Moreover, a soft or fuzzy classification may be derived to more appropriately represent land cover than the conventional hard classification.     

Sub-pixel land cover mapping for per-field classification

A method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a perfield classification based on hard per-pixel classified imagery.     

Study on Crops Remote Sensing Classification based onMulti-temporal Landsat 8 OLI Images

It is crucial for agricultural production to know crop planting situation.Temporal remote sensing images and subtle spectral characteristics of ground features play an important role in extracting crops distribution.At this point,multi-temporal Landsat 8 OLI images were used to extracting the distribution of main crops in the east of Xinrong district of Datong city by using Spectral Angle Mapper(SAM) combined with the decision tree classification,and the extracting result was compared with the result that maximum likelihood extracted.The results show that：① The planting area of spring corn,grain,soybean and potato is decreased and mosaic distribution in order.② The overall accuracy obtained by SAM combined with the decision tree classification is 85.34% and the Kappa coefficient is 0.76,which is outperformed the results of maximum likelihood with the increase of 22.51% and 0.31,respectively,the classification results was more consistent with the actual distribution of main crops.③ The classification accuracy of main crops used the multi-temporal remote sensing images was obviously higher than that of single-temporal image,and the difference between ground features and spectra in middle or high resolution images can effectively weaken by analyzing multi-temporal data from the perspective of difference of spectral angle.The results not only confirmed the positive effect of multi-temporal remote sensing images on crops classification,but also developed the SAM combined with decision tree classification in crops classification of medium-high resolution remote sensing images,which has a certain application prospect.     

采用分段主成分和PPI的高光谱影像分类

高光谱遥感影像波段多且存在混合像元,特征提取以及端元提取都是高光谱影像分类必不可少的工作,分类方法的选择也是因地适宜。以福建省泉州市德化县下属某一地区的CASI影像为实验数据,基于分段主成分(segmental principal component analysis,SPCA)和纯净像元指数法(pure pixel index,PPI),提出了最小距离(minimum distance classification,MDC)和二进制编码(binary encoding,BE)的高光谱影像分类方法。实验结果表明,MDC的总体精度为69.71%,BE的总体精度为70.88%。对单一地物精度而言2种方法各有其长,MDC对道路的分类精度更高,为98.08%;而植被、耕地和水体采用BE方法的分类精度更高,分别为94.12%、98.08%、98.11%。本文提出的方法应用于CASI高光谱影像,对该研究区的地物分类研究有一定的实用性和参考价值。