面向对象的泰安市高分辨率影像住宅建筑物提取

基于eCognition Developer平台,以泰安市QuickBird影像为数据,采用面向对象多尺度分割、最邻近和隶属度分类,充分利用高分辨率QuickBird影像具有的丰富光谱、形状、纹理和结构等地物信息,对实验区进行分类并提取住宅建筑物信息。实验表明,与传统逐像元分类法相比,面向对象分类法有效地避免了分割区域的离散破碎,地类信息的提取更加完整、精确、高效。     

JSEG改进算法在多光谱遥感影像区域分割上的应用

图像分割是对图像进行感兴趣区域提取与识别的基础,是图像分析的关键步骤.基于区域分割的JSEG算法是一种既融合了颜色信息又融合了空间信息的图像分割方法,在普通图像和视频图像中都能得到良好的分割结果.将这一算法引入到遥感影像的分割中,并对其做出改进,使之适用于多光谱遥感影像和纹理特征复杂的遥感影像的区域分割.实验结果表明,该算法很好的解决了由于影像中复杂的地物信息而产生的不同区域边界模糊的问题.     

A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.     

基于增强DeepLabV3网络的高分辨率遥感影像分类

由于高分影像具有地物细节丰富、类别差异大等特点,现有的卷积神经网络影像分类方法普遍存在分类精度低、地物边界不准确等问题。鉴于此,本文提出一种基于增强DeepLabV3网络的影像分类模型。首先构建R-MCN网络结构,利用大小不同的卷积核并结合残差网络的思想进一步提取浅层网络的多尺度、多层次的特征信息;然后采用可学习的上采样方式,并将R-MCN提取的特征与高阶的语义信息相融合;最后通过提出的Mloss损失函数,获得遥感影像的地物分类结果。试验结果表明,相对于传统的卷积神经网络,本文方法能细化地物的边缘信息,改善分类效果,获得更高的影像分类精度。     

结合nDSM的高分辨率遥感影像深度学习分类方法

针对高分辨率遥感影像因其地物类内差异大、光谱信息相对欠缺导致现有影像分类方法存在错分现象较多、地物边界残缺不完整等问题,本文提出了一种归一化数字表面模型（nDSM）约束的高分辨率遥感影像深度学习分类方法。首先,将nDSM数据作为附加波段叠加在遥感影像上并获取训练样本;然后,利用优化的U-Net网络进行模型训练得到最优模型;最后,利用最优模型对附加了nDSM波段的遥感影像进行地物分类。试验结果表明,本文方法引入nDSM数据用于U-Net模型训练和分类,可有效提高影像分类精度,得到更加真实可靠的分类结果。     

基于不同分辨率遥感影像的分类方法对比研究

基于4种不同分辨率的遥感影像数据,分别为30 m分辨率的Landsat-8数据,融合Landsat-8多光谱波段和全色波段的15 m分辨率数据,5.8 m分辨率的资源3号卫星数据以及融合后2.1 m分辨率的资源3号卫星数据。采用ISO-DATA、最大似然分类法和面向对象分类法对影像进行分类,对分类方法的效果以及分辨率变化对面向对象分类方法的精度影响进行分析。结果显示在低分辨率影像中,面向对象方法受到限制,分类效果相比传统方法没有太大改善;而在高分辨率影像中,面向对象方法分类效果很好,并且随着分辨率提高分类精度也相应的提高。     

GF-2影像面向对象典型城区地物提取方法

国产高分遥感影像信息丰富,提供了精准的地物空间细节,深入研究高分数据处理及其提取城区地类目标信息的方法具有重要意义。本文以国产高分二号（GF-2）遥感影像为数据源,利用规则集的面向对象分类方法,通过ESP尺度分析工具选取得出最优分割尺度,建立各类地物的特征体系及分类规则,最终提取出研究区典型城区地物信息,并将之与传统基于像元的SVM监督分类结果作比较。结果表明：规则集的面向对象分类总体精度为92.23%,Kappa系数为0.9,比SVM监督分类有大幅度提高。对高分二号等高分辨率影像,面向对象的分类方法精度更高,图示效果更好,是城区地物提取的有效方法。     

综合多特征的极化SAR图像随机森林分类算法

为抑制相干斑噪声对极化SAR图像分类结果的干扰,本文提出一种综合多特征的极化SAR图像随机森林分类方法。该方法首先利用简单线性迭代聚类(SLIC)算法生成超像素作为分类单元;然后,基于高维极化特征图像,利用训练好的随机森林模型,统计决策树的分类投票数,计算各超像素的类别概率;最后,利用超像素间的空间邻域特征,采用概率松弛算法(PLR)迭代修正超像素的类别后验概率,并依据最大后验概率(MAP)准则得到分类结果;实现综合利用超像素和空间邻域特征,降低相干斑噪声干扰的极化SAR图像分类方法。实验对比结果表明:本文方法能得有效抑制极化SAR图像中相干斑噪声的干扰,得到高精度且光滑连续的分类结果。     

Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: A case study in an urban–rural landscape in the Brazilian Amazon

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban–rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method.     

不同降维策略下的高光谱影像多特征分类

由于物体表面的空间分布通常是富有规律且局部连续的,在高光谱影像分类中应充分利用其光谱和空间信息.本文在对高光谱影像立方体进行降维处理的基础上,提出了一种联合空域和谱域信息的高光谱影像高效分类方法.首先,分别选用主成分分析(Principal Component Analysis,PCA)和正交投影波段选择(Orthog...     

A fuzzy topology-based maximum likelihood classification

Classification is one of the most widely used remote sensing analysis techniques, with the maximum likelihood classification (MLC) method being a major tool for classifying pixels from an image. Fuzzy topology, in which the set concept is generalized from two values, {0, 1}, to the values of a continuous interval, [0, 1], is a generalization of ordinary topology and is used to solve many GIS problems, such as spatial information management and analysis. Fuzzy topology is induced by traditional thresholding and as such gives a decomposition of MLC classes.Presented in this paper is an image classification modification, by which induced threshold fuzzy topology is integrated into the MLC method (FTMLC). Hence, by using the induced threshold fuzzy topology, each image class in spectral space can be decomposed into three parts: an interior, a boundary and an exterior. The connection theory in induced fuzzy topology enables the boundary to be combined with the interior. That is, a new classification method is derived by integrating the induced fuzzy topology and the MLC method. As a result, fuzzy boundary pixels, which contain many misclassified and over-classified pixels, are able to be re-classified, providing improved classification accuracy. This classification is a significantly improved pixel classification method, and hence provides improved classification accuracy.     

Some issues related with sub-pixel classification using HYSI data from IMS-1 satellite

The mixed pixels are treated as noise or uncertainty in class allocation of a pixel and conventional hard classification algorithms may thus produce inaccurate classification outputs. Thus application of sub-pixel or soft classification methods have been adopted for classification of images acquired in complex and uncertain environment. The main objective of this research work has been to study the effect of feature dimensionality using statistical learning classifier — support vector machine (SVM with sigmoid kernel) while using different single and composite operators in fuzzy-based error matrixes generation. In this work mixed pixels have been used at allocation and testing stages and sub-pixel classification outputs have been evaluated using fuzzy-based error matrixes applying single and composite operators for generating matrix. As subpixel accuracy assessment were not available in commercial software, so in-house SMIC (Sub-pixel Multispectral Image Classifier) package has been used. Data used for this research work was from HySI sensor at 506 m spatial resolution from Indian Mini Satellite-1 (IMS-1) satellite launched on April 28, 2008 by Indian Space Research Organisation using Polar Satellite Launch Vehicle (PSLV) C9, acquired on 18^th May 2008 for classification output and IRS-P6, AWIFS data for testing at sub-pixel reference data. The finding of this research illustrate that the uncertainty estimation at accuracy assessment stage can be carried while using single and composite operators and overall maximum accuracy was achieved while using 40 (13 to 52 bands) band data of HySI (IMS-1).     

结合UPSCALING技术与对象多特征的土地利用覆盖信息提取研究

应用SPOT融合数据,以北京密云地区为例,提出了整合Upscaling技术与对象多特征方法的新思路,通过基于半变异函数的 空间变异特征分析,建立了面向对象多特征与多分辨率数据集的多尺度分类决策树,并对自动分类效率进行了初步探讨。     

一种简单加入空间关系的实用图像分类方法

遥感图像分类是遥感图像处理的一项基本内容，也是遥感应用中关键的一步。为了提高分类的精度，一方面是对光谱信息的合理利用；另一方面，可以加入新的信息源，即进行多源数据处理，并加入地学知识，尤其是对空间信息的利用是至关重要的。但是由于地学知识的复杂性及空间信息利用的难度以及数据源的限制，尚无公认的实用方法。该文提出了一种简单加入空间关系的分类方法，在没有其它数据源的情况下，利用空间关系特性，在分类中构造两个空间关系波段，实现空间约束，部分消除仅依赖光谱数据分类而引起的同物异谱和同谱异物造成的分类错误。简单实用，同时也验证了空间关系在分类中的重要性。     

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.     

遥感尺度内涵及遥感像元尺度效应分析的改进分形方法

本文首先分析了遥感影像尺度的三层次内涵。重点针对遥感像元尺度,分析了遥感像元尺度效应及其分形机理,由于现有分形方法没有考虑影像本身尺度(空间分辨率),造成尺度间分形维数的比较时像元尺度效应变化难以有效反映,本文针对此问题提出了基于表面积的加窗分形布朗运动和加窗双层地毯两种改进分形方法。为验证改进分形方法的可靠性,采用了不同像元尺度下系列监督分类进行验证。试验结果表明,每种地物的分维数都随着空间分辨率的降低或像元尺度的缩小,呈总体下降趋势,在某些特征尺度上会出现预示着某些地物结构的拐点,这些拐点对观测该区域地物具有一定指示意义。系列监督分类精度也一定程度上证明了以上两种改进分形方法在分析尺度效应中的可行性。因此本文的方法对于分析遥感像元尺度效应和探索地物尺度聚合规律具有一定的理论意义。     

Segmentation and classification of high spatial resolution images based on Hölder exponents and variance

Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions. In this study, Hölder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture. A threshold is derived to segment the transformed image into textured and non-textured regions. Subsequently, the original image is extracted into textured and non-textured regions using this segmented image mask. Afterward, extracted textured region is classified using ISODATA classification algorithm considering HE, VAR, and intensity values of individual pixel of textured region. And extracted non-textured region of the image is classified using ISODATA classification algorithm. In case of non-textured region, HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes. Consequently, the classified outputs of non-textured and textured regions that are generated independently are merged together to get the final classified image. IKONOS 1 m PAN images are classified using the proposed algorithm, and the classification accuracy is more than 88%.     

基于CNN模型迁移的OLI影像光伏电池板场景识别

获取光伏电池板的空间分布及动态变化信息对于国土调查、资源环境监测和能源结构评估具有重要意义,然而,传统的光伏电池板的识别依赖于人工设计的中低层次特征,无法克服对象光谱不确定性、空间结构类型复杂等难题,算法普遍存在稳健性不强、效率不高等问题。目前,基于场景单元从遥感影像中提取空间信息,多数算法仅建立在少数标准数据集上,未考虑实际应用中遥感图像质量、空间分辨率等因素对图像场景深度特征表达的影响,制约了遥感技术在城市结构、经济社会知识挖掘方面的深入应用。针对以上情况,本文基于卷积神经网络（CNN）采用迁移学习和模型微调的策略,在中等分辨率的Landsat影像上进行光伏电池板场景识别。结果表明,本文方法能够提取电站场景的多层次特征,在形态结构复杂的电站场景中取得了较好的识别效果。     

Improved IHS Algorithm for Fusing High Resolution Satellite Images of Urban Areas

The intensity-hue-saturation method is used frequently in image fusion due to its efficiency and high spatial quality. The main shortage is its spectral distortion stemmed from replacement of intensity band with higher resolution image. In this study, a new method is introduced to improve the spectral quality of the Intensity-Hue-Saturation (IHS) algorithm. The goal of this study is to produce the fused image that has a better spectral and spatial quality with respect to the original images in term of visual comparison and the classification result. In this regard, an improved statistical approach is developed to combine an intensity band from IHS algorithm and an input high resolution image such as SAR or Panchromatic image. Then the intensity image is replaced by the combined image band. Final fused images are attained using the inverse IHS algorithm. The proposed fusion algorithm is tested on two data sets of: a) panchromatic and multi spectral bands of IKONOS image with the same acquisition date, and b) multi spectral and HH bands of IKONOS and TerraSAR-X images respectively with different acquisition dates. Moreover, the obtained results are compared with other fusion methods like IHS, Gungor, Brovey and synthetic variable ratio. The results show less spectral discrepancy of the proposed method comparing to other methods. Finally, the outcome of proposed method is classified and classification overall accuracy is improved by 5.6 and 2 percentage for data set ‘a’ and ‘b’ respectively.     

面向对象的高光谱遥感影像稀疏表示分类

稀疏表示用于高光谱遥感影像分类多是基于像素层次来处理的。文中提出一种面向对象的高光谱遥感影像稀疏表示分类方法。首先从高光谱影像中提取4个波段组成标准的多波段影像,进行面向对象的影像分割;然后计算各对象在各波段上的光谱均值,并选取少量样本进行训练;最后利用基于Fisher字典学习的稀疏表示进行高光谱遥感影像的分类。实验结果表明,该方法可以利用较少的样本得到较好的分类效果,与基于像素层的稀疏分类相比较,分类精度与效率均有所提高,分类结果更接近真实地物,避免了零碎图斑。