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1.
针对高光谱影像分类面临的小样本问题,提出了一种深度少样例学习算法,该算法在训练过程中通过模拟小样本分类的情况来训练深度三维卷积神经网络提取特征,其提取得到的特征具有较小类内间距和较大的类间间距,更适合小样本分类问题,且能用于不同的高光谱数据,具有更好的泛化能力。利用训练好的模型提取目标数据集的特征,然后结合最近邻分类器和支持向量机分类器进行监督分类。利用Pavia大学、Indian Pines和Salinas 3组高光谱影像数据进行分类试验,试验结果表明,该算法能够在训练样本较少的情况下（每类地物仅选取5个标记样本作为训练样本）取得优于传统半监督分类方法的分类精度。 相似文献
2.
深度学习在高光谱图像处理领域的研究应用不断深入发展,基于深度学习的高光谱图像分类达到了较高的分类精度。目前的分类模型多利用高光谱的图谱特征,但对光谱的诊断性特征及先验信息利用不足,对空谱特征提取过程难以实现有效协同,因而导致分类类别即类内分类不够精细。为了解决以上问题,本文提出一种以多标签数据为输入的共生神经网络模型,在高光谱图谱特征提取的基础上融合光谱诊断特征,实现相对含水量反演及精细分类。首先,通过构建一种新的红边斜率光谱指数实现高光谱图像相对含水量的表征,利用本文提出的自适应分级算法完成相对含水量反演并建立对应的等级标签,与地物种类标签共同构成多标签高光谱数据集。然后,构建共生神经网络架构及内部变维特征提取模块,利用多标签数据提取高光谱图像中空间、光谱和相对含水量的融合特征,提高深度模型对不同含水量地物的区分能力和对所提取特征的协同表达能力,降低模型的复杂度与计算量,完成基于多标签数据集的相对含水量反演引导分类的过程,在扩大传统类间距离的基础上进一步扩大类内距离,从而实现高光谱图像的精细分类。最后,使用实验室采集数据和4个公开的高光谱数据集Lopex、Indian Pines、Pavia University和Salinas进行实验验证。结果表明,本文提出的红边斜率光谱指数可以有效表征地物的相对含水量信息;相对含水量反演引导的分类模型对类内分类精度有较明显的提升,对总体分类结果有一定的改善;与其他机器学习和深度学习分类算法相比,本文算法取得了较好的分类结果,提高了深度分类模型的分类性能和精细程度,实现了精细分类。 相似文献
3.
Parham Pahlavani Mahdi Hasanlou Siamak Talebi Nahr 《Journal of the Indian Society of Remote Sensing》2017,45(1):11-23
Currently, hyperspectral images have potential applications in many scientific areas due to the high spectral resolution. Extracting suitable and adequate bands/features from high dimensional data is a crucial task to classify such data. To overcome this issue, dimension reduction techniques have direct effects to improve the efficiency of classifiers on hyperspectral images. One common approach for decreasing the dimensionality is the feature/band selection by considering the optimum dimensionality of the hyperspectral imagery. In this paper, a new method was proposed to select optimal band for classification application, based on a metaheuristic Invasive Weed Optimization (IWO) algorithm. In this regard, the K-nearest neighbour (K-NN) technique was used as the classifier. Moreover, as a by-product of our band selection method, a new method was proposed to estimate an optimum dimension of the reduced hyperspectral images for better classification. Experimental results over three real-world hyperspectral datasets clearly showed that the proposed IWO-based band selection algorithm of this study led to the significant progress in selecting suitable bands for classification applications and estimation of optimum dimensionality of these datasets. In this regard, the overall accuracy (OA) of classification of the proposed IWO-based band selection algorithm was 92.02, 93.57, and 89.72 % for each dataset, respectively. Moreover, results reveal the superiority of the proposed IWO-based band selection algorithm against the other algorithms including GA, SA, ACO, and PSO for band selection purpose. 相似文献
4.
高光谱影像特征的利用率对提高其分类精度具有重要意义。为充分利用影像的特征,提出了一种特征重标定网络的高光谱影像分类方法。该方法通过全局平均池化将特征图转换为具有全局信息的实数,利用全连接层与非线性层生成能够代表各通道相对重要性的权值,进而采取加权法完成初始特征的重标定。为验证该方法的有效性,选取PaviaU和KSC两组高光谱影像数据进行实验。结果表明,提出方法总体分类精度分别达到98.38%和95.61%,可为高光谱影像提供有效的类别判定特征,有助于提高影像分类精度并获取平滑的分类结果图。 相似文献
5.
精准农田识别是农作物估产和粮食安全评估的基础。遥感数据作为农田识别的重要数据源,可提供动态、快速的监测结果。高光谱数据在农田识别分类方面具有巨大的应用潜力,但其中的冗余波段影响了分类效率和分类精度。因此,本研究提出了一种适用于高光谱数据农田分类的混合式特征选择算法。首先,基于变量的重要性排序或约束程度,按步长逐步进行降维;其次,寻找分类精度骤减的转折点,并将其对应的变量作为特征子集;最后,利用序列后向选择SBS（Sequential Backward Selection）方法搜索最优分类特征子集。本研究利用GF-5高光谱数据,共研究了3种降维方法（随机森林RF（Random Forest）、互信息MI（Multi-Information）和L1正则化（L1 regularization））和3种分类算法（随机森林、支持向量机SVM（Support Vector Machine）和K近邻KNN（K-Nearest Neighbor））的组合在农田分类中的表现。结果表明,基于L1正则化法得到的特征子集自相关性较低,并且包含的红边和近红外波段有效提高了农田、森林和裸土的区分度。在不同分类模型比较中发现,SVM在高维空间中表现出非常好的抗噪能力,分类精度高于RF和KNN。而RF在低维空间中的泛化能力要高于SVM和KNN。相比于第一步降维得到的特征子集,使用SBS搜索得到的最优特征子集均提高了分类精度。最终,具有23维输入的L1-SVM-SBS分类模型得到了最高的总体分类精度（94.64%）和农田召回率（95.83%）。本研究为高光谱数据特征优选提供了一种新思路,筛选出了更具代表性的特征波段,提高了农田分类精度,对高光谱遥感分类研究具有参考价值。 相似文献
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7.
针对当前特征提取方法不能充分挖掘高光谱影像稀疏特性的问题,提出一种基于稀疏判别分析的高光谱影像特征提取方法。首先,在线性判别分析的系数向量中引入稀疏正则项来捕获具有更强判别能力的特征,将高光谱影像映射至低维稀疏的子空间;然后,利用迭代优化方法对模型进行求解。利用Salinas和Pavia University高光谱影像进行对比实验,所提方法与分类方法结合用于影像分类时,其分类精度优于其他方法,总体分类精度分别达到97.42%和97.64%。 相似文献
8.
Exploiting spectral and spatial information in hyperspectral urban data with high resolution 总被引：2，自引：0，他引：2
Dell'Acqua F. Gamba P. Ferrari A. Palmason J.A. Benediktsson J.A. Arnason K. 《Geoscience and Remote Sensing Letters, IEEE》2004,1(4):322-326
Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures. 相似文献
9.
The recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones. 相似文献
10.
Taskin Kavzoglu Hasan Tonbul Merve Yildiz Erdemir Ismail Colkesen 《Journal of the Indian Society of Remote Sensing》2018,46(8):1297-1306
Object-based image analysis (OBIA) has attained great importance for the delineation of landscape features, particularly with the accessibility to satellite images with high spatial resolution acquired by recent sensors. Statistical parametric classifiers have become ineffective mainly due to their assumption of normal distribution, vast increase in the dimensions of the data and availability of limited ground sample data. Despite pixel-based approaches, OBIA takes semantic information of extracted image objects into consideration, and thus provides more comprehensive image analysis. In this study, Indian Pines hyperspectral data set, which was recorded by the AVIRIS hyperspectral sensor, was used to analyse the effects of high dimensional data with limited ground reference data. To avoid the dimensionality curse, principal component analysis (PCA) and feature selection based on Jeffries–Matusita (JM) distance were utilized. First 19 principal components representing 98.5% of the image were selected using the PCA technique whilst 30 spectral bands of the image were determined using JM distance. Nearest neighbour (NN) and random forest (RF) classifiers were employed to test the performances of pixel- and object-based classification using conventional accuracy metrics. It was found that object-based approach outperformed the traditional pixel-based approach for all cases (up to 18% improvement). Also, the RF classifier produced significantly more accurate results (up to 10%) than the NN classifier. 相似文献
11.
对高光谱数据进行波段组合,可以减少信息量的冗余,提高数据的处理速度。对黄河口入海口湿地进行分类,对合理利用、开发保护该地区湿地资源具有重要意义。本文首先分析了“珠海一号”高光谱数据各个波段的信息量及波段之间的相关系数,然后利用最佳波段指数(OIF)方法选出波段组合B7-B8-B32,进一步在OIF基础上设置信息量与相关系数阈值,选出波段组合B7-B18-B32,实验结果证明分类精度提高了5.4%。最后,根据地物的光谱特征分析,选择光谱差异较大的波段进行组合B6-B13-B18,分类后精度比OIF筛选出的波段组合精度高12.6694%。经实验验证,结合地物光谱特征的波段组合可以大大提高分类精度。 相似文献
12.
基于随机森林特征优选的冬小麦分类方法 总被引：1，自引：0，他引：1
本文基于多时相Landsat 8 OLI数据,进行综合光谱、植被指数的特征提取与特征选择的方法研究。通过分析光谱与植被指数特征时序变化,提取最佳时相光谱,构建小麦提取特征;采用基于重要性与Pearson相关性的随机森林特征选择算法优选特征。结果表明:利用优选特征分类时,总体精度为89.78%,小麦分类精度为98.33%;与优选前特征的分类结果相比,精度分别提高了2.96%、2.55%;基于重要性与Pearson相关性的随机森林特征选择提高了分类精度和分类器工作效率。 相似文献
13.
高光谱影像光谱-空间多特征加权概率融合分类 总被引：3，自引：3，他引：0
提出了一种基于光谱-空间多特征加权概率融合的高光谱影像分类方法。首先,利用最小噪声分离(minimum noise fraction,MNF)方法对高光谱影像进行降维和特征提取,并以得到的MNF特征影像作为光谱特征,联合灰度共生矩阵(gray level co-occurrence matrix,GLCM)提取的纹理特征、基于OFC算子建立的多尺度形态学特征以及采用连续最大角凸锥(sequential maximum angle convex cone,SMACC)提取的端元组分特征,组成3组光谱-空间特征;然后利用支持向量机(support vector machine,SVM)对每一组光谱-空间特征进行分类,得到每组特征的概率输出结果;最后,建立多特征加权概率融合模型,应用该模型将不同特征的概率输出结果进行加权融合,得到最终分类结果。为了验证该方法的有效性,利用ROSIS和 AVIRIS影像进行试验,总体分类精度分别达到97.65%和96.62%。结果表明本文的方法不但较好地克服了传统基于单一特征高光谱影像分类的局限性,而且其分类效果也优于常规矢量叠加(vector stacking,VS)和概率融合的多特征分类方法,有效地改善了高光谱影像的分类结果。 相似文献
14.
面向对象的遥感影像多层次迭代分类方法研究 总被引：3，自引：0，他引：3
在分析应用对象化分析方法改进高空间分辨率遥感影像分类技术的基础上,提出了应用多层次的迭代模型改进分类流程,在自适应的迭代过程中有效地结合主导类别选择、高级对象特征计算、基于互信息的特征选择等技术提高对象化方法中丰富的影像特征的利用效率,同时,有机结合像素级特征信息弥补对象化特征。通过对SPOT5影像与航空影像两种数据源... 相似文献
15.
联合空-谱信息的高光谱影像深度三维卷积网络分类 总被引：4，自引：2，他引：2
针对高光谱影像分类高维和小样本的特点,提出一种基于深度三维卷积神经网络的高光谱影像分类方法。首先,该方法直接以高光谱数据立方体为输入,利用三维卷积操作提取高光谱数据立方体的三维空-谱特征。然后,利用残差学习构建深层网络,提取更高层次的特征表达,以提高分类精度。最后,采用Dropout正则化方法防止过拟合。利用Pavia大学、Indian Pines和Salinas 3组高光谱数据进行试验验证,结果表明,与支持向量机和现有的基于深度学习的高光谱影像分类方法相比,该方法能有效提高高光谱影像的地物分类精度。 相似文献
16.
Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data 总被引：2，自引：0，他引：2
Feature selection is a key task in remote sensing data processing, particularly in case of classification from hyperspectral images. A logistic regression (LR) model may be used to predict the probabilities of the classes on the basis of the input features, after ranking them according to their relative importance. In this letter, the LR model is applied for both the feature selection and the classification of remotely sensed images, where more informative soft classifications are produced naturally. The results indicate that, with fewer restrictive assumptions, the LR model is able to reduce the features substantially without any significant decrease in the classification accuracy of both the soft and hard classifications 相似文献
17.
郭学兰;杨敏华;毛军;周秋琳 《东北测绘》2013,(4):144-146,149+152
针对高光谱影像数据具有波段众多、数据量较大的特点,本文提出了一种基于波段子集的独立分量分析(ICA)特征提取的高光谱遥感影像分类的新方法。以北京昌平小汤山地区的高光谱影像为例,根据高光谱遥感影像的相邻波段的相关性进行子空间划分,在各个波段子集上采用ICA算法进行特征提取,将各个子空间提取的特征合并组成特征向量,采用支持向量机(SVM)分类器进行分类。结果表明:该方法分类精度最佳(分类精度89.04%,Kappa系数0.8605,明显优于其它特征提取方法的SVM分类,有效地提高了高光谱数据的分类精度。 相似文献
18.
高光谱遥感数据具有光谱信息丰富、图谱合一的特点,目前已经广泛地应用在对地观测中。传统的高光谱分类模型大多过分依赖影像光谱信息,没有充分利用空间特征信息,这使得分类精度还有很大的提升空间。条件随机场是一种概率模型,能够较好地融合空间上下文信息,在高光谱影像分类中已经得到越来越多的关注,但大部分条件随机场模型存在超平滑的现象,会导致影像细节丢失。针对该问题,本文提出了一种优化融合影像空-谱信息的高分辨率/高光谱影像分类方法,该方法将影像的纹理信息与原始光谱信息进行融合,利用SVM分类器对其进行预分类,并将各类概率定义为一元势函数,以融合空间特征信息;然后将空间平滑项和局部类别标签成本项加入二元势函数中,以考虑空间背景信息,并保留各类别中的详细信息。最后,通过两组的高分辨率/高光谱影像数据进行试验。结果表明,与SVM算法、传统的条件随机场方法和面向对象的分类方法相比,本文提出的算法在整体分类精度上分别提高了10%、9%和8%以上,同时在保持地物边缘完整性、避免“同谱异物”与“同物异谱”的现象方面有较明显的优势。 相似文献
19.
Karim Saheb Ettabaa Manel Ben Salem 《Journal of the Indian Society of Remote Sensing》2018,46(2):157-167
One of the challenging problems in processing high dimensional data, as hyperspectral images, with better spectral and temporal resolution is the computational complexity resulting from processing the huge amount of data volume. Various methods have been developed in the literature for dimensionality reduction, generally divided into two main techniques: data transformation techniques and features selection techniques. The feature selection technique is advantageous compared to transformation techniques in preserving the original data. However, deciding the appropriate number of features to be selected and choosing these features are very challenging since they require exhaustive researches. The progressive feature selection technique is a new concept recently introduced to address these issues based on priority criteria. However, this approach presents limits when these criteria are insufficient or depends on domain applications. In this paper, we present a new approach to improve the Progressive Feature Selection technique by adding new criteria that measure the amount of information present in each band. The endmembers extraction phase of the proposed approach includes both the N-FINDR and the ATGP algorithms. A case based reasoning system is used to choose the optimal criterion for the endmember extraction. The performances of this proposed approach were evaluated using AVIRIS hyperspectral image and the obtained results prove its effectiveness compared to other PBS techniques. 相似文献
20.
Behnaz Bigdeli Farhad Samadzadegan Peter Reinartz 《Journal of the Indian Society of Remote Sensing》2013,41(4):763-776
With recent technological advances in remote sensing sensors and systems, very high-dimensional hyperspectral data are available for a better discrimination among different complex land-cover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or ‘curse of dimensionality’ in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods. 相似文献