排序方式： 共有44条查询结果，搜索用时 15 毫秒
1.
波段选择是高光谱遥感图像分类的重要前提,本文提出了一种用于高光谱遥感图像波段选择的改进二进制布谷鸟算法,通过使用混合二进制编码算法更新子代鸟巢和使用遗传算法交叉方式更新被发现鸟巢两个方面对二进制布谷鸟算法进行改进,找出在图像中起主要作用且相关性低的波段,实现对高光谱遥感图像降维。将本文算法运用于PaviaU数据集和AVIRIS数据集,并与二进制布谷鸟算法、二进制粒子群算法、最小冗余最大相关算法、Relief算法等进行对比分析。结果表明,改进二进制布谷鸟算法波段特征选择效率更高,且选取的波段更具代表性,能够较好地提高后续分类精度。 相似文献
2.
高光谱遥感影像数据量大、波段数多,容易导致“维数灾难”。传统流形学习方法一般仅考虑其光谱特征,忽略了空间信息。为此提出一种非监督的基于加权空-谱联合保持嵌入(WSCPE)的维数约简算法。首先采用加权均值滤波(WMF)方法对高光谱影像进行滤波,以消除噪点和背景点的干扰。然后根据遥感影像地物分布的空间一致性,通过采用加权空-谱联合距离(WSCD)来融合像素点的光谱信息和空间信息,有效选取各像素点的空-谱近邻,并根据像素点与其空-谱近邻点之间的坐标距离来有区别的利用其近邻点进行流形重构,提取低维鉴别特征进行地物分类。在PaviaU和Indian Pines数据集上的分类结果表明,总体分类精度分别达到了98.89%和95.47%。该方法在反映影像内部流形结构的同时,有效融合了影像的空间-光谱信息,故能提高影像特征的鉴别性,并提升分类性能。 相似文献
3.
Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas. 相似文献
4.
针对高光谱数据预处理中传统降维算法的不足,文章提出采用线性局部切空间排列(LLTSA)算法进行降维,并在低维空间中,以数据点到背景流形的最小距离为度量进行异常目标检测。面向异常目标检测问题的降维算法,需要考虑计算量和异常污染两个问题:为减少计算量,选择图像中一部分具有代表性的训练数据进行LLTSA降维并求取用于泛化的投影矩阵;为避免异常信息对背景特性的影响,应该选择不含异常的背景训练数据建立背景流形。背景训练点的选择基于递归多层分割算法,结合分割块的大小和分割块被近邻点重构的误差,去除分割结果中可能包含异常的区域并尽可能多地保留背景信息。实验结果表明LLTSA可以利用少数特征有效区分背景和异常,基于LLTSA的检测算法比经典RX和核RX算法具有更好的异常检测性能。 相似文献
5.
Forecasting weather parameters such as temperature and pressure with a reasonable degree of accuracy three hours ahead of
the scheduled departure of an aircraft helps economic and efficient planning of aircraft operations. However, these two parameters
exhibit a high degree of persistency and have nonstationary mean and variance at sub-periods (i.e. at 0000, 0300, 0600,…,
2100UTC). Hence these series have been standardised (to have mean 0 and variance 1) and thereafter seasonal differenced (lag
8) to achieve almost near stationarity. An attempt has been made to fit the standardised and seasonal differenced series of
Chennai (a coastal station) and Trichy (an inland station) airport into an Auto Regressive (AR) process. The model coefficients
have been estimated based on adaptive filter algorithm which uses the method of convergence by the steepest descent. The models
were tested with an independent data set and diagnostic checks were made on the residual error series. An independent estimation
of fractal dimension has also been made in this study to conform the number parameters used in the AR processes. The models
contemplated in this study are parsimonious and can be used to forecast surface temperature and pressure. 相似文献
6.
为了提高滑坡的预测精度,通过对灰色GM（1,1）模型与BP神经网络模型各自优缺点及互补性的分析,建立了GM—BP串联组合预测模型。模型首先采用等维动态GM（1,1）模型进行初步预测,然后利用BP神经网络对初步预测的结果进行训练及仿真,通过数据的归一化处理,参数的判定选取,获得组合模型预测值。以茅坪滑坡为例,对位移进行了预测。通过数据的对比分析,发现GM—BP串联组合预测模型在短期预测精度上高于单一模型。 相似文献
7.
粒子滤波自从被引入资料同化领域以来,对于高维系统存在的粒子衰退问题一直困扰着资料同化领域的研究。隐式等权重粒子滤波（Implicit Equal-Weights Particle Filter,IEWPF）通过在高维的状态空间维数的前提下,隐式从每个粒子都具有特殊协方差的提议密度中进行采样,构建等权重的粒子集合,从而解决高维系统的粒子衰退问题。通过在高维准地转模式中应用IEWPF方法,验证了IEWPF的系统一致性和资料同化效果。通过对水平动能谱的检验,验证了IEWPF可以保持系统的原始平衡特性。通过IEWPF与等权重粒子滤波（Equivalent Weights Particle Filter,EWPF）的对比试验发现,两者的资料同化分析场非常接近,但在运行效率上,IEWPF远优于EWPF。同时,IEWPF也为解决一系列的资料同化问题,比如参数估计,提供了新的解决途径。 相似文献
8.
AbstractHyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required. 相似文献
9.
高光谱影像空-谱协同嵌入的地物分类算法 总被引：4，自引：4，他引：0
针对传统高光谱影像地物分类算法大多仅考虑光谱信息而忽略空间邻近像元间相关性的问题,提出了一种空-谱协同嵌入(SSCE)降维算法和空-谱协同最近邻(SSCNN)分类器。首先,定义一种空-谱协同距离,并将其应用于近邻选取和低维嵌入;然后,构建空-谱近邻关系图来保持数据中的流形结构,并在权值设置中增大空间近邻点的权重以增强数据间的聚集性,提取鉴别特征;最后使用SSCNN分类器对降维后的数据进行分类。利用PaviaU和Salinas高光谱数据集进行试验验证,结果表明,与传统的光谱分类算法相比,该算法能有效提高高光谱影像的地物分类精度。 相似文献
10.
Effects of galvanic distortion on magnetotelluric data over a three-dimensional regional structure 总被引：2，自引：0，他引：2
Although the galvanic distortion due to local, near-surface inhomogeneities is frequency-independent, its effect on the magnetotelluric data becomes, in a 3-D structure, frequency-dependent. Therefore, both the apparent resistivity and the phase responses are disturbed, and a correction should be carried out prior to the 3-D interpretation in order to retrieve the 3-D regional impedance tensor. In many cases, the structure is 2-D for depths corresponding to a first range of periods and 3-D for longer periods (called 2-D/3-D). For these cases, a simple method which allows us to retrieve the 3-D regional impedance tensor (except the static shift) is presented. The method proposed uses the Groom & Bailey decomposition of the distortion matrix for the short periods. Three examples are presented: two using synthetic data and one employing real data. These examples show the effect of the galvanic distortion over a regional 2-D/3-D model and the retrieval of the regional transfer functions from the distorted ones. 相似文献