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1.
高光谱遥感影像分类是高光谱遥感影像处理和应用的重要组成部分。然而,高光谱遥感影像具有波段数量较多和空间分辨率较高等特点,给分类任务带来一定的挑战。为了提高分类精度,充分利用影像的空间信息和像素间的局部信息,提出一种引导滤波联合局部判别嵌入的高光谱影像分类方法。首先,对高光谱遥感影像进行归一化,利用主成分分析方法实现特征提取,将提取的第一主成分影像作为引导图像;其次,采用引导滤波分别提取各波段影像的空间特征;然后,将提取的空间影像特征进行叠加,通过局部Fisher判别分析完成低维嵌入;最后,将得到的低维嵌入特征输入支持向量机分类器得到分类结果。采用Indian Pines和Pavia University两幅高光谱影像进行实验的结果表明：在分别从各类地物中随机选取10%和100个样本作为训练样本的情况下,其总体分类精度分别提高到98.28%和99.45%;对比其他相关方法,该方法能够获取更高的分类精度。该方法在低维嵌入的同时,有效利用了影像的空间信息,改善了分类效果。 相似文献
2.
Detection of changes in synthetic aperture radar (SAR) images is an important challenge due to the effects of speckle noise on these images. In recent years, appropriate methods for SAR-based-change detection have been developed based on the level set methods (LSM). These methods need to set parameters for defining a proper initial contour. Moreover, the gradient information is only employed in the total energy of these methods for segmentation of the difference image. In this study, a novel method has been proposed for unsupervised change detection of multitemporal SAR images based on the improved fast level set method (IFLSM) initialized with a combination of k-means and Otsu techniques. The proposed method utilizes the discrete wavelet transform (DWT) fusion strategy and edge enhancement to achieve a noise-resistant difference image from the mean-ratio and log-ratio images. Afterward, the generated binary change map (CM) by applying a combination of k-means and Otsu techniques on the difference image is used as the initial contour to achieve a final CM on difference image using the IFLSM. To check advantages of the proposed method, experiments are applied on two sets of multitemporal SAR images corresponding to artificial Chitgar Lake (under reconstruction) in Tehran (Iran) taken by TerraSAR-X satellite in 2011 and 2012, and corresponding to San Pablo and Briones reservoirs in California (USA) acquired by ERS-2 satellite in 2003 and 2004. Results of proposed method were compared with results of some well-known unsupervised change detection methods. Experimental results prove the sufficiency of the proposed method in unsupervised change detection in terms of accuracy, implementation time, and computational complexity. 相似文献
3.
Lan Liu Cheng-fan Li Yong-mei Lei Jing-yuan Yin Jun-juan Zhao 《Arabian Journal of Geosciences》2017,10(14):307
Principal component analysis (PCA) and independent component analysis (ICA) are linear feature extraction methods in terms of the second-order statistics and higher-order statistics and have good compatibility and complementarity. For the feature extraction of the hyperspectral remote sensing image, an approach of the combined PCA and ICA was followed in the real remote sensing classification applications. In this study, the weighted PCA-ICA method was introduced to extract the feature information from HJ-1A hyperspectral imager (HSI) image. And then the real airborne visible infrared imaging spectrometer (AVIRIS) image case was performed by the distance similarity measure. Experimental results on HJ-1A HSI and AVIRIS images indicate that the proposed method can get high average accuracy of 89.55% and kappa coefficient of 0.8101 than the typical methods under certain condition with a suitable number of eigenvectors and weighted values. 相似文献
4.
遥感图像分类是提取图像有效信息过程中重要的一部分,为了探寻最优的分类方法,许多机器学习算法逐步应用于遥感分类中。极限学习机（extreme learning machine,ELM）以其高效、快速和良好的泛化性能在模式识别领域得到广泛应用。本文采用训练速度快、运算量小的极限学习机算法与支持向量机（support vector machines,SVM）算法和最大似然法进行分类对比,对高分辨率遥感图像进行分类,分析极限学习机算法对于遥感图像分类的准确度等性能。选取吉林省长春市部分区域的GF-2遥感数据,将融合后的影像设置为原始数据,利用3种方法进行分类。研究结果表明,极限学习机算法分类图像总体分类精度达到85%以上,kappa系数达到0.718,与其他分类方法相比分类准确度较高,且极限学习机运行时间比支持向量机运行时间约短2 480 s,约为支持向量机运行时间的1/8,因此具有良好的性能和实用价值。 相似文献
5.
为提高采用遥感影像监测开采沉陷演化的准确性,探讨了基于多维纹理特征的影像分类方法。首先提取影像的多维纹理特征:局部方差、局部平均梯度、局部能量和局部信息熵,然后将其与地物光谱值一并作为人工免疫算法中样本的特征向量,利用免疫算法的选择、克隆、变异算子进行自学习得到全局最优聚类中心,从而提高影像分类精度。对淮南煤田进行开采沉陷遥感监测,结果表明,该方法分类总精度为88.26%,Kappa系数为0.853,优于传统的Parallelepiped和Maximum likelihood分类方法。 相似文献
6.
Zeyad Makhamreh 《Environmental Earth Sciences》2018,77(10):384
The mountainous region represents the most important agricultural and biodiversity haven in Jordan. The objective of this study is to characterize the seasonal pattern of land use and vegetation using multi-temporal SPOT images. Multi-temporal SPOT images were analyzed to characterize the land use and cropping pattern in the mountain regions of Jordan. The images were radiometrically corrected using invariant objects located on the image, and a linear inter-calibration method was used to calibrate the other images. A hybrid classification approach was used in the classification; the spectral signatures of the land-use classes were derived in an iterative procedure using the ISODATA and field survey data. Then, the maximum likelihood classification was applied on all images to classify the class signatures into thematic land-use types. The hybrid classification approach gives more accurate classification accuracy especially for the multi-seasonal image classification. The overall accuracy of the multi-temporal data set was achieved with 87.9%, while classification accuracy for single-date classifications were 61.3, 76.8, 72.2, and 65.5 for months of October, February, April, and June, respectively. In addition, the scene combinations that were derived from February and April were classified the land-use types almost as well as those combinations including more scenes. Regarding the classification details, the multi-temporal images enable higher level of classification for land-use types such as Anderson level 2, and produce accurate boundaries for the different cropping and farming systems. 相似文献
7.
The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face. 相似文献
8.
野外地质剖面考察是开展地质研究的一项重要工作。在野外地质考察过程中,为了直观展示地质剖面的细节信息,往往需要拍摄大量的影像。同时,为了提高细节信息的展示程度,这些影像往往分辨率高,但视场角小,所以单一的影像注定无法兼顾的展示地质剖面的整体信息和细节信息。因此基于这种同时展示整体和细节信息的需求前提下,本文在自主研发的Mosaic of Image Program(MIP)图像拼接系统的基础上,对野外拍摄的影像进行了拼接处理,形成地质剖面的长条带全景影像。为了提高拼接处理的精度,本文对高精度的相机检校、畸变校正及MIP的地质剖面应用等方面进行了研究。在湖南古丈县地质遗迹剖面的实际处理中,完成了300多张高分辨率影像的快速拼接,获得了良好的数据记录和展示效果,拼接影像几何失真小于2像元。本文实现的技术手段在野外地质剖面考察中具有良好的应用前景。 相似文献
9.
高分辨率遥感影像场景分类一直是遥感领域的研究热点.针对遥感场景对尺度的需求具有多样性的问题,提出了一种基于多尺度循环注意力网络的遥感影像场景分类方法.首先,通过Resnet50提取遥感影像多个尺度的特征,采用注意力机制得到影像不同尺度下的关注区域,对关注区域进行裁剪和缩放并输入到网络.然后,融合原始影像不同尺度的特征及其关注区域的影像特征,输入到全连接层完成分类预测.此分类方法在UC Merced Land-Use和NWPU-RESISC45公开数据集上进行了验证,平均分类精度较基础模型Resnet50分别提升了1.89%和2.70%.结果表明,多尺度循环注意力网络可以进一步提升遥感影像场景分类的精度. 相似文献
10.
X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.
相似文献11.
Remotely sensed image analysis using spectral-spatial information plays a key role in modern remote sensing applications. This article presents a new semi-automatic framework for spectral-spatial classification of hyperspectral images. The proposed framework benefits from a combination of pixel-based and object-based classification scenarios in which the main parameters are adaptively tuned. In order to reduce the complexity of the method, an unsupervised band selection technique is used as well. Meanwhile, the wavelet thresholding is applied in order to smooth the selected bands. The classification results after applying the proposed method to well-known standard hyperspectral datasets are better than those of the most of the other state-of-the-art approaches. As an example, the overall classification accuracy achieved by applying the proposed semi-automatic spectral-spatial classification framework to the Salinas dataset is more than 99% for 10% training samples per class. Moreover, the vital parameters are adaptively set in our approach. 相似文献
12.
Thomas Oommen Debasmita Misra Navin K. C. Twarakavi Anupma Prakash Bhaskar Sahoo Sukumar Bandopadhyay 《Mathematical Geosciences》2008,40(4):409-424
Accurate thematic classification is one of the most commonly desired outputs from remote sensing images. Recent research efforts
to improve the reliability and accuracy of image classification have led to the introduction of the Support Vector Classification
(SVC) scheme. SVC is a new generation of supervised learning method based on the principle of statistical learning theory,
which is designed to decrease uncertainty in the model structure and the fitness of data. We have presented a comparative
analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised
classification technique. SVC is an optimization technique in which the classification accuracy heavily relies on identifying
the optimal parameters. Using a case study, we verify a method to obtain these optimal parameters such that SVC can be applied
efficiently. We use multispectral and hyperspectral images to develop thematic classes of known lithologic units in order
to compare the classification accuracy of both the methods. We have varied the training to testing data proportions to assess
the relative robustness and the optimal training sample requirement of both the methods to achieve comparable levels of accuracy.
The results of our study illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality
issues such as the Hughes Effect. 相似文献
13.
14.
夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。 相似文献
15.
Spectral unmixing is a key technology of optical remote sensing image analysis; it not only influences the accuracy of the extraction of land cover information and automatic classification of topographical objects, but also greatly hinders the development of quantitative remote sensing. Independent component analysis (ICA) is a statistical method which is recently developed to extract the independent linear components, and which can realize the extraction of endmembers as well as fractional abundances with little a priori knowledge. However, ICA still cannot process the correlations among the various components. To overcome this problem, variational Bayesian independent component analysis (VBICA) has been proposed to process optical remote sensing images. In the Bayesian framework, the separation of independent components of remote sensing image has finally been achieved with conditional independence standards of Bayesian network and approximate variational algorithm. In the simulative image and real AVIRIS hyperspectral remote sensing image, the VBICA algorithm demonstrates its better performance. The experiment’s results indicate that the proposed VBICA algorithm is feasible, which has obvious advantages and a good application prospect. The reason is that it can effectively overcome the correlations between the various components in remote sensing images and break through the limitations of traditional remote sensing images analysis. Last but not least, the VBICA algorithm is applied in the classification of the TM multispectral remote sensing images. Compared to basic maximum likelihood classification, principal component analysis and FastICA algorithms, VBICA improves the classification accuracy of remote sensing images, and contributes to the further extension of the application of ICA in remote sensing image analysis. 相似文献
16.
基于ASTER数据遥感影像的决策树分类 总被引：6，自引：0，他引：6
以黑龙江省北安市为研究区域,尝试利用ASTER视反射率值进行便利、准确的土地利用分类研究。对ASTER数据进行波段相关分析,确定最佳组合波段;然后重点分析转换为视反射率值的影像特征和光谱特征,从中提取各种典型地物的光谱曲线; 并依据提取的光谱曲线建立基于地物反射率值大小关系或阈值的决策树模型,对研究区不同地物类型进行分类,并对结果进行精度评价。应用效果表明,该方法简单有效,但对于混合光谱容易错分。 相似文献
17.
地理信息系统与主成分分析在多年气象观测数据处理中的应用 总被引：7，自引：0，他引：7
探讨一种处理多年气象观测数据的新方法,该方法以地理信息系统为技术支撑,以数字图像处理和标准主成分分析为核心,适用于处理空间分布广、时间序列长的多类型气象观测数据。在实例研究中,首先对中国160个站10年气温、降水观测数据进行空间双线性插值,得到一系列数值图像;并对这些数值图像进行标准主成分分析,得到反映多年气候状况且相互独立的各主成分;然后选取其中前6个主成分进行空间迭代聚类分析,得到中国气候分类图;通过对所得类别进行多年气候指标统计分析,得到中国气候区划图;最后将所得气候区划图与传统气候区划图进行比较分析,并指出该方法在处理多年气象观测数据工作中的可行性与局限性。 相似文献
18.
Image classification is one of the crucial techniques in detecting the crops from remotely sensed data. Crop identification and discrimination provide an important basis for many agricultural applications with various purposes, such as cropping pattern analysis, acreage estimation, and yield estimation. Accurate and faster estimation of crop area is very essential for projecting yearly agriculture production for deciding agriculture policies. Remote sensing is a technique that allows mapping of large areas in a fast and economical way. In many applications of remote sensing, a user is often interested in identifying the specific crop only while other classes may be of no interest. Indian Remote Sensing Satellite (IRS-P6) LISS IV sensor image of spatial resolution 5.8 m has been used to identify the sugarcane crop for the Chhapar village of Muzaffarnagar District, India. Classification of satellite data is one of the primary steps for information extraction for crop land identification. In recent years, decision tree approach to image analysis has been developed for the assessment and improvement of traditional statistically based image classification. In this study, ISODATA, MLC, and vegetation indices based decision tree approaches are used for classifying LISS IV imagery. The 11 vegetation index images have been generated for decision tree classification. All the three methods are compared and it is found that the best performance is given by the decision tree method. Vegetation indices based decision tree method for sugarcane classification, the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient were found 88.17, 86.59, and 87.93% and 0.86 respectively. 相似文献
19.
研究聚类分析新方法一直是统计学和机器学习研究领域普遍关注的课题。针对概率距离聚类算法不能解决非线性可分聚类问题的缺欠,笔者应用核函数理论将该模型拓展成为一种能够解决非线性可分聚类问题的统计模型,称为核概率距离聚类分析模型。研制出一种应用新模型进行遥感图像非监督分类研究的实施策略和可行算法;在GDAL遥感图像数据输入输出函数库基础上,用VC++语言开发了遥感图像核概率距离聚类分析算法程序;用ERDAS软件提供的一幅7波段491像素×440像素大小的TM图像进行新方法分类应用实验研究。对比了新模型和其原版本的TM遥感图像非监督分类效果,结果表明新模型的非监督分类效果优于原有的分类模型。 相似文献
20.
为有效解决传统遥感图像变化检测预处理复杂的问题,提出一种基于随机补片和DeepLabV3+的建筑物遥感图像变化检测方法。以ResNet50特征提取网络为基础,创建DeepLabV3+语义分割网络,并在图像和标签中创建大小为224像素×224像素的随机补片作为网络输入,训练建筑物提取网络;修改建筑物提取网络输入层为6通道,通过矩阵运算将两期遥感图像转换为一幅6通道非RGB图像,利用转换后的非RGB图像进行网络训练并验证变化检测精度。实验1利用ENVI5.3软件,采用马氏距离法进行变化检测;实验2采用改进的U-Net网络和随机补片,完成网络训练和精度验证;实验3使用实验2的训练数据和验证数据,采用随机补片和DeepLabV3+网络进行变化检测网络训练及精度验证。实验结果表明,该方法实验1、实验2、实验3建筑物变化检测平均交并比分别为24.43%、83.14%、89.90%,边界轮廓匹配分数分别为61.47%,80.24%、96.51%。 相似文献