A Markov random field model for classification of multisourcesatellite imagery

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery     

基于最小一乘的遥感图像融合

基于全色数据与多光谱数据之间的线性关系进行遥感图像融合是一种可行并被广泛应用的思路.本文利用GCOS模型对GS方法进行了分析,发现GS法可通过最小二乘法进行线性回归.由于最小一乘与最小二乘相比更为稳健,因此预期采用最小一乘法的融合方珐具有更好的性能.本文提出了一种基于最小一乘融合方法的构建,并对IKONOS数据进行了对比试验.试验结果表明,基于最小一乘的融合方法是对GS方法的一种有效改进.     

一种采用高斯隐马尔可夫随机场模型的遥感图像分类算法

该文研究了无监督遥感图像分类问题。文中构造了图像的隐马尔可夫随机场模型(HiddenMarkov Random Fleid,HMRF),并且提出了基于该模型的图像分类算法。该文采用有限高斯混合模型(Finite Gaussian Mixture,FGM)描述图像像素灰度的条件概率分布,使用EM(Expectation-Maximization)算法解决从不完整数据中估计概率模型参数问题。针对遥感图像分布的不均匀特性,该文提出的算法没有采用固定的马尔可夫随机场模型参数,而是在递归分类算法中分级地调整模型参数以适应区域的变化。实验结果表明了该文算法的有效性,分类算法处理精度高于C-Means聚类算法.。     

Classification of remotely sensed images using decimal coded morphological profiles

In this paper, we propose a novel method for pixel classification of remotely sensed images. The proposed method exploits the spatial information of image pixels using morphological profiles produced by structuring elements of different sizes and shapes. Morphological profiles produced by multiple structuring elements are combined into a single feature by decimal coding. The advantage of proposed feature is that it can effectively utilize the potential of multiple morphological profiles without increasing the complexity of feature space. The proposed approach was tested on remotely sensed images with known ground truths, and performance was improved up to 27 % in the overall accuracy results over existing techniques.     

受昆虫视觉启发的多光谱遥感影像小目标检测

现有多光谱遥感影像目标检测算法大多依赖于结构化背景模型和先验信息,背景复杂化和先验信息匮乏将导致高虚警率的检测结果。受昆虫视觉系统中小目标检测神经元的启发,跳出传统研究思路,提出多光谱遥感影像小目标仿生检测模型及相应的目标检测方法。该方法利用神经元非线性滤波特性对突变信号的敏感性,在局部区域内通过背景纹理抑制和目标边缘增强实现目标检测。实验结果表明该方法在高复杂度背景条件下获得较为稳定的低虚警率检测效果。同时该算法可以较好地平衡背景复杂度和空间分辨率之间的矛盾关系,相比现有检测算法还具有原理简单、易于实现等特点。     

Integrated method for boundary delineation of agricultural fieldsin multispectral satellite images

Most agricultural statistics are calculated per field, and it is well known that classification procedures for homogeneous objects produce better results than per-pixel classification. In this study, a multispectral segmentation method for automated delineation of agricultural field boundaries in remotely sensed images is presented. Edge information from a gradient edge detector is integrated with a segmentation algorithm. The multispectral edge detector uses all available multispectral information by adding the magnitudes and directions of edges derived from edge detection in single bands. The addition is weighted by edge direction, to remove "noise" and to enhance the major direction. The resulting edge from the edge detection algorithm is combined with a segmentation method based on a simple ISODATA algorithm, where the initial centroids are decided by the distances to the edges from the edge detection step. From this procedure, the number of regions will most likely exceed the actual number of fields in the image and merging of regions is performed. By calculating the mean and covariance matrix for pixels of neighboring regions, regions with a high generalized likelihood-ratio test quantity will be merged. In this way, information from several spectral bands (and/or different dates) can be used for delineating field borders with different characteristics. The introduction of the ISODATA classifier compared with a previously used region growing procedure improves the output. Some results are compared with manually extracted field boundaries     

Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery.

This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.     

一种基于集成学习和特征融合的遥感影像分类新方法

针对多源遥感数据分类的需要,提出了一种基于全极化SAR影像、极化相干矩阵特征、光学遥感影像光谱和纹理的多种特征融合和多分类器集成的遥感影像分类新方法.对全极化PALSAR数据进行预处理和极化相干矩阵特征提取,利用灰度共生矩阵计算光学和SAR影像的对比度、逆差距、二阶距、差异性等纹理特征参数,并与光谱特征结合,形成6种组合策略.利用集成学习方法对随机森林分类器、子空间分类器、最小距离分类器、支持向量机分类器、反向传播神经网络分类器等分类器进行组合,对不同组合策略的遥感影像特征集进行分类.结果表明提出的基于多种特征和多分类器集成的新方法很好地利用了主被动遥感数据在不同地表景观类型提取上的潜力,综合了多种算法的优势,能够有效地提高总体精度和各类别的分类精度.     

基于SVM混合核的遥感图像变化检测

针对由实际遥感地物类型难以确定导致的多光谱遥感影像变化检测精度较低的问题,提出一种基于SVM混合核的遥感图像变化检测。首先利用CVA算法构造差异影像,其次利用灰度共生矩阵提取差异影像的纹理特征与差异影像的灰度特征组成特征向量,接着利用差异影像的直方图选择置信度高的训练样本,并利用构造的SVM混合核进行训练得到分类超平面,最后利用SVM混合核函数对差异影像进行二分类得到最后的变化检测结果。实际遥感数据验证结果表明,所构造的SVM混合核函数用于多光谱遥感影像变化检测中是可行、有效的。     

Spectral and spatial decorrelation of Landsat-TM data for losslesscompression

Presents some new techniques of spectral and spatial decorrelation in lossless data compression of remotely sensed imagery. These techniques provide methods to efficiently compute the optimal band combination and band ordering based on the statistical properties of Landsat-TM data. Experiments on several Landsat-TM images show that using both the spectral and the spatial nature of the remotely sensed data results in significant improvement over spatial decorrelation alone. These techniques result in higher compression ratios and are computationally inexpensive     

A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method.     

Improved image classification with neural networks by fusing multispectral signatures with topographical data

Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans succeed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat multispectral images of the Black Hills. Bands 4, 5, 6, and 7 were used to generate four classifications based on the spectral signatures. A file of georeferenced ground truth classifications was used as the training criterion. The network was trained to classify urban, agricultural, range, and forest with a 65.7% correctness. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topographical file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7% and 75.7%.     

Scale aware remote sensing image enhancement using rolling guidance

Enhancement of remotely sensed images is a challenging problem, since the enhanced image has to have an improved contrast and edge information while preserving the original radiance values as much as possible. In this paper, a scale aware enhancement method based on rolling guidance is proposed for remotely sensed images. For each scale, a guidance image is defined and the approximation image is provided by an iterative joint filtering of the approximation and guidance images. Then the extracted details are amplified through an adaptive scheme and added to the final level approximation layer to provide the resulting enhanced image. A comparative study between the proposed methods with classical edge preserving filters and traditional methods have been carried out by using several criteria. The proposed methods have an average of 12% improvement for contrast gain (CG) metric and 81% improvement for enhancement measurement (EME) metric compared to the closest comparison method.     

Nonlinear estimation of scene parameters from digital images usingzero-hit-length statistics

A zero-hit run-length probability model for image statistics is derived. The statistics are based on the lengths of runs of pixels that do not include any part of objects that define a scene model. The statistics are used to estimate the density and size of the discrete objects (modeled as disks) from images when the image pixel size is significant relative to the object size. Using different combinations of disk size, density, and image resolution (pixel size) in simulated images, parameter estimation may be used to investigate the essential invertibility of object size and density. Analysis of the relative errors and 95% confidence intervals indicates the accuracy and reliability of the estimates. An integrated parameter r, reveals relationships between errors and the combinations of the three basic parameters of object size, density, and pixel size. The method may be used to analyze real remotely sensed images if simplifying assumptions are relaxed to include the greater complexity found in real data     

An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images

The paper presents a novel adaptive fuzzy evidential nearest neighbor formulation for classifying remotely sensed images. The formulation combines the generalized fuzzy version of the Dempster-Shafer evidence theory (DSET) and the K-nearest neighbor (KNN) algorithm. Each of the K nearest neighbors provides evidence on the belongingness of the input pattern to be classified, and it is evaluated based on a measure of disapproval to achieve the adaptive capability during the classification process. The disapproval measure quantifies the lack of support with respect to the belongingness of the input pattern to a given class. Pieces of evidence are ranked based on their degree of disapproval and fused in a sequential manner. The pignistic Shannon entropy is used to estimate the degree of consensus among pieces of evidence provided by nearest neighbors and as a criterion for terminating the evidence fusion process. The paper reports the results of experimental work conducted to evaluate the proposed classification scheme using real multichannel remote sensing images. As will be demonstrated using the experimental results, the proposed classification scheme demonstrated robust performance and outperformed commonly used methods such as the K-nearest neighbor algorithm of Cover and Hart (1967), the fuzzy K-nearest neighbor algorithm of Keller et al. (1985), the evidence-theoretic K-nearest neighbor algorithm of Denoex (1995), and its fuzzy version of Zouhal and Denoex (1997). The performance of these techniques is examined with respect to the K-parameter and classification accuracy.     

Consensual and Hierarchical Classification of Remotely Sensed Multispectral Images

Consensual and hierarchical approaches are developed for the classification of remotely sensed multispectral images. The proposed method consists of preprocessing of input patterns, generating multiple classification results by hierarchical neural networks, and a combining scheme to generate a consensus of multiple classification results. Transformations of input patterns by random matrices and nonlinear filtering are used for preprocessing. By varying the input patterns, the multiple classification results are generated with sufficiently independent errors by using a single type of classifier. This helps to improve classification performance when the multiple classification results are combined. Hierarchical neural networks involve the use of successive classifiers which are tuned to reduce the remaining errors to increase the classification performance. This structure includes detection schemes to decide whether successive classifiers are utilized for each input. Consensual and hierarchical approaches generate more reliable and accurate results based on group decision.     

Use of Neural Networks for Automatic Classification From High-Resolution Images

The effectiveness of multilayer perceptron (MLP) networks as a tool for the classification of remotely sensed images has been already proven in past years. However, most of the studies consider images characterized by high spatial resolution (around 15-30 m) while a detailed analysis of the performance of this type of classifier on very high resolution images (around 1-2 m) such as those provided by the Quickbird satellite is still lacking. Moreover, the classification problem is normally understood as the classification of a single image while the capabilities of a single network of performing automatic classification and feature extraction over a collection of archived images has not been explored so far. In this paper, besides assessing the performance of MLP for the classification of very high resolution images, we investigate on the generalization capabilities of this type of algorithms with the purpose of using them as a tool for fully automatic classification of collections of satellite images, either at very high or at high-resolution. In particular, applications to urban area monitoring have been addressed     

基于多特征融合的红外图像分类研究

针对传统红外图像目标分类方法准确率低的问题,提出了一种用结合多特征融合的粒子群优化(Particle Swarm Optimization, PSO)算法来优化支持向量机(Support Vector Machine, SVM)的方法。该方法采用方向梯度直方图(Histogram of Oriented Gradient, HOG)和局部二值模式(Local Binary Pattern, LBP)两类特征描述红外图像中目标的轮廓特征和局部纹理,从不同的方面展现红外图像的特点,在图像的特征表达上具有一定的互补性。在特征提取后对样本数据进行凸包算法计算,得到一些具有代表性的样本数据,从而提高分类计算效率;在分类模型训练时,采用PSO算法优化SVM,寻找SVM的最优惩罚因子和核参数,从而提高分类模型的准确率。实验结果表明,多特征融合的分类模型的准确率比单一特征的分类模型提高近10%,且经PSO优化的SVM最终模型的分类准确率高达99%。     

基于独立成分分析的图像颜色特征提取研究

结合独立成分分析（Independent Components Analysis,ICA）和最大相似度分类器（Maximum Likelihood）的特点,本文提出了一种基于ICA的多频谱遥感图像色彩分类的算法.该算法提取图像的色彩的独立成分,去除了图像的R、G、B之间的相关性,光谱独立成份用来聚集像素,使用Maximum Likelihood对像素进行颜色分类.实验结果表明,该方法识别性能好,准确度高,是对多频谱遥感图像的颜色特征提取的一种有效方法.     

高光谱-LiDAR 融合的条件随机场分类方法

为有效利用高光谱影像与LiDAR数据的互补性信息,解决单一融合策略造成的场景解译地物边界不准确和分类精度低的问题,提出了一种光谱-空间-高度特征融合、并顾及场景地物类别共生特性的条件随机场分类方法。首先,对两种数据分别提取光谱及形态学特征,对特征集采用图模型进行特征融合,将特征输入概率支持向量机分类器,得到初始分类结果。然后,基于融合特征计算反映像素间类别本质差异的局部光谱-空间-高度协同的异质性值,并统计类别间的空间共生关系。最后,在条件随机场框架内,整合初始分类结果、局部异质性信息及类别共生关系,通过目标函数的迭代求解获得最终分类结果。通过将像素间的权重定义为对应像素位置融合特征的归一化欧式距离的单调减函数,对标记不同但特征差异较大的类别间给予较小的权重,以达到地物边界空间规整化的目的。通过对标记不同但共生概率较大的类别对给予较小的权重,达到保留空间关系稳定的类别对的目的。采用城区场景的美国休斯顿地区数据集和林区场景的中国广西高峰林场两组数据集对提出方法进行了验证。实验结果表明:休斯顿和高峰林场数据集精度分别达到94.00%和92.84%,分类结果的“胡椒盐”现象明显减少,证明了该方法的有效性。