基于DS聚类的高光谱图像集成分类算法

针对高光谱遥感图像维数高、样本少导致分类精度低的问题,提出一种基于DS聚类的高光谱图像集成分类算法(DSCEA)。首先,根据高光谱数据特点,从整体波段中随机选择一定数量的波段,构成不同的训练样本;其次,分析图像的空谱信息,构造无向加权图,利用优势集(DS)聚类方法得到最大特征差异的波段子集;最后,根据不同样本,利用支持向量机训练具有差异的单个分类器,采用多数表决法集成最终分类器,实现对高光谱遥感图像的分类。在Indian Pines数据集上DSCEA算法的分类精度最高可达到84.61%,在Pavia University数据集上最高可达到91.89%,实验结果表明DSCEA算法可以有效的解决高光谱分类问题。     

面向分类应用的高光谱谱段选择方法

高光谱数据在物质分类识别领域得到了广泛应用,但存在数据量大、波段间相关性高等问题,严重影响分类精度及应用。针对以上问题分析了已有的波段选择方法,提出了基于波段聚类及监督分类的遗传算法,对高光谱数据进行波段选择：采用[K]均值聚类算法对波段数据进行聚类分析,构造波段子集合;利用分类器族分类精度构造适应度函数,采用遗传算法对波段子集合进行优化选择。最后用阔叶林高光谱数据对提出的算法进行对比实验,实验结果表明针对分类应用,提出的算法能够非常有效地选择高光谱谱段。     

Band Selection of Hyperspectral Images for Automatic Detection of Poultry Skin Tumors

This paper presents a spectral band selection method for feature dimensionality reduction in hyperspectral image analysis for detecting skin tumors on poultry carcasses. A hyperspectral image contains spatial information measured as a sequence of individual wavelength across broad spectral bands. Despite the useful information for skin tumor detection, real-time processing of hyperspectral images is often a challenging task due to the large amount of data. Band selection finds a subset of significant spectral bands in terms of information content for dimensionality reduction. This paper presents a band selection method of hyperspectral images based on the recursive divergence for the automatic detection of poultry carcasses. For this, we derive a set of recursive equations for the fast calculation of divergence with an additional band to overcome the computational restrictions in real-time processing. A support vector machine is used as a classifier for tumor detection. From our experiments, the proposed band selection method shows high detection accuracy with low false positive rates compared to the canonical analysis at a small number of spectral bands. Also, compared with the enumeration approach of 93.75% detection rate, our proposed recursive divergence approach gives 90.6% detection rate, which is within the industry-accepted accuracy of 90-95%, while achieving the computational saving for real-time processing.     

分段2维主成分分析的超光谱图像波段选择

目的 超光谱图像具有极高的谱间分辨率,巨大的数据量给分类识别等后续处理带来很大压力。为了有效降低图像数据维数,提出基于分段2DPCA的超光谱图像波段选择算法。方法 首先根据谱间相关性对原始图像进行波段分组,然后根据主成分反映每个光谱波段的信息比重分别对每组图像进行波段选择,从而实现超光谱图像的谱间降维。结果 该算法有效降低了超光谱图像的光谱维数,选择的波段明显反映出不同地物像元矢量的区别。结论 实验结果表明,该波段选择算法相对传统算法速度更快,并且较好地保留了原始图像的局部重要信息,对后续处理有积极意义。     

Phase correlation based redundancy removal in feature weighting band selection for hyperspectral images

Feature weighting based band selection provides a computationally undemanding approach to reduce the number of hyperspectral bands in order to decrease the computational requirements for processing large hyperspectral data sets. In a recent feature weighting based band selection method, a pair‐wise separability criterion and matrix coefficients analysis are used to assign weights to original bands, after which bands identified to be redundant using cross correlation are removed, as it is noted that feature weighting itself does not consider spectral correlation. In the present work, it is proposed to use phase correlation instead of conventional cross correlation to remove redundant bands in the last step of feature weighting based hyperspectral band selection. Support Vector Machine (SVM) based classification of hyperspectral data with a reduced number of bands is used to evaluate the classification accuracy obtained with the proposed approach, and it is shown that feature weighting band selection with the proposed phase correlation based redundant band removal method provides increased classification accuracy compared to feature weighting band selection with conventional cross correlation based redundant band removal.     

基于类别可分性的高光谱图像波段选择

高光谱遥感数据具有丰富的光谱信息,应用十分广泛,但其冗余的光谱信息有时会限制高光谱图像的分类等的精度以及计算复杂度.为了提高解译效率,高光谱图像降维不可或缺,这也是高光谱图像处理的研究热点之一.提出了一种基于类别可分性的高光谱图像波段选择方法(Endmember Separability Based band Selection,ESBB),该方法通过Mahalanobis距离最大化图像中各类地物的可分性来确定最优的波段组合.相较于其他监督波段选择算法,该方法不需要大量训练样本,不用对每个组合做分类处理.对波段选择后的结果进行分类的实验结果证明,该方法是一个快速有效的波段选择方法,可以得到一个较好的分类精度.     

A non-negative low-rank representation for hyperspectral band selection

Hyperspectral images are widely used in real applications due to their rich spectral information. However, the large volume brings a lot of inconvenience, such as storage and transmission. Hyperspectral band selection is an important technique to cope with this issue by selecting a few spectral bands to replace the original image. This article proposes a novel band selection algorithm that first estimates the redundancy through analysing relationships among spectral bands. After that, spectral bands are ranked according to their relative importance. Subsequently, in order to remove redundant spectral bands and preserve the original information, a maximal linearly independent subset is constructed as the optimal band combination. Contributions of this article are listed as follows: (1) A new strategy for band selection is proposed to preserve the original information mostly; (2) A non-negative low-rank representation algorithm is developed to discover intrinsic relationships among spectral bands; (3) A smart strategy is put forward to adaptively determine the optimal combination of spectral bands. To verify the effectiveness, experiments have been conducted on both hyperspectral unmixing and classification. For unmixing, the proposed algorithm decreases the average root mean square errors (RMSEs) by 0.05, 0.03, and 0.05 for the Urban, Cuprite, and Indian Pines data sets, respectively. With regard to classification, our algorithm achieves the overall accuracies of 77.07% and 89.19% for the Indian Pines and Pavia University data sets, respectively. These results are close to the performance with original images. Thus, comparative experiments not only illustrate the superiority of the proposed algorithm, but also prove the validity of band selection on hyperspectral image processing.     

A semi-supervised spatially aware wrapper method for hyperspectral band selection

Band selection is widely used to identify relevant bands for land-cover classification of hyperspectral images. The combination of spectral and spatial information can improve the classification performance of hyperspectral images dramatically. Similarly, the fusion of spectral–spatial information should also improve the performance of band selection. In this article, two semi-supervised wrapper-based spectral–spatial band selection algorithms are proposed. The local spatial smoothness of hyperspectral imagery is used to improve the performance of band selection when limited labelled samples available. With superpixel segmentation, the first algorithm uses the statistical characteristics of classification map to predict the classification quality of all samples. Based on the Markov random field model, the second algorithm incorporates the spatial information by the minimization of spectral–spatial energy function. Four widely used real hyperspectral data sets are used to demonstrate the effectiveness of the proposed methods, when compared to cross-validation-based wrapper method, the accuracy is improved by 2% for different data sets.     

Multidimensional local spatial autocorrelation measure for integrating spatial and spectral information in hyperspectral image band selection

Hyperspectral band selection aims at the determination of an optimal subset of spectral bands for dimensionality reduction without loss of discriminability. Many conventional band selection approaches depend on the concept of “statistical distance” measure between the probability distributions characterizing sample classes. However, the maximization of separability does not necessarily guarantee that a classification process results in the best classification accuracies. This paper presents a multidimensional local spatial autocorrelation (MLSA) measure that quantifies the spatial autocorrelation of the hyperspectral image data. Based on the proposed spatial measure, a collaborative band selection strategy is developed that combines both spectral separability measure and spatial homogeneity measure for hyperspectral band selection without losing the spectral details useful in classification processes. The selected band subset by the proposed method shows both larger separability between classes and stronger spatial similarity within class. Case studies in biomedical and remote sensing applications demonstrate that the MLSA-based band selection approach improves object classification accuracies in hyperspectral imaging compared with conventional approaches.     

Band selection and evaluation with spatial information

Spatial information has been widely used for hyperspectral image classification, which can dramatically improve the classification accuracy. Though band selection is an important pre-processing step for hyperspectral image processing, spatial information has not been well exploited in this field. In this article, we will exploit the spatial information for band selection. This article mainly includes two parts: algorithm design, and algorithm evaluation. In the first part, we propose an efficient band selection method by using the spatial structure information and spectral information. In the second part, we advocate the use of the local spatial filtering and the spectral-spatial classifier for evaluating the performance of band selection algorithms instead of the traditional pixel-wise classifiers. Comprehensive experiments over diverse publicly available benchmark data sets reveal some interesting results.     

使用深度对抗子空间聚类实现高光谱波段选择

高光谱图像（HSI）由数百个波段组成,波段之间的相关性强且具有较高的冗余度,导致出现维度灾难并且分类的复杂性很高。为此,使用深度对抗子空间聚类（DASC）网络进行高光谱的波段选择,并引入拉普拉斯正则化使网络更优,在保证分类精度的前提下降低分类的复杂度。该网络通过在编码器和解码器中引入自表达层来模仿传统子空间聚类的“自表达”属性,充分运用光谱信息和非线性特征转换得到波段之间的相互关系,解决传统波段选择方法无法同时考虑光谱和空间信息的问题。同时,引入对抗学习来监督自编码器的样本表示和子空间聚类,使得子空间聚类具有更好的自表达性能。为了使网络性能更优,加入拉普拉斯正则化来考虑反映图像几何信息的局部流形结构。实验在两个公开的高光谱数据集上进行,所提出的方法和几种主流的波段选择方法进行对比的结果表明,DASC方法在分类精度上优于对比方法,其选出的波段子集可以满足应用需求。     

New framework for hyperspectral band selection using modified wind-driven optimization algorithm

The presence of irrelevant and highly correlated spectral bands significantly reduces the classification accuracy of the hyperspectral images. Therefore, the selection of suitable bands from the set of available spectral bands plays a crucial role in improving the classification accuracy. In this paper, a novel band selection approach is proposed based on nature inspired meta-heuristic algorithm to mitigate the effect of curse of dimensionality. Wind-driven optimization (WDO), among other meta-heuristic algorithms, has proven to be more efficient in solving global optimization problems. However, WDO is prone to premature convergence when solving the global optimization problem due to loss of diversity of air particles. Therefore, a modified WDO (MWDO) is proposed for band selection, which is able to avoid the premature convergence and control the exploration–exploitation search trade-off. Finally, in order to further improve the performance of the classification, the selected bands are fed into the deep learning architecture to extract the high-level useful features. The experiments are carried on three widely used standard datasets such as Indian Pines, Pavia University, and Salinas. The experimental results show that the proposed approach selects an optimal subset of bands with good convergence characteristics and provide high classification accuracy with fewer bands in comparison with other approaches. The proposed method achieves an overall accuracy of 93.26%, 94.76%, and 95.96% for Indian Pines, Pavia University, and Salinas datasets, respectively.     

Fast hyperspectral band selection based on spatial feature extraction

Hyperspectral images usually consist of hundreds of spectral bands, which can be used to precisely characterize different land cover types. However, the high dimensionality also has some disadvantages, such as the Hughes effect and a high storage demand. Band selection is an effective method to address these issues. However, most band selection algorithms are conducted with the high-dimensional band images, which will bring high computation complexity and may deteriorate the selection performance. In this paper, spatial feature extraction is used to reduce the dimensionality of band images and improve the band selection performance. The experiment results obtained on three real hyperspectral datasets confirmed that the spatial feature extraction-based approach exhibits more robust classification accuracy when compared with other methods. Besides, the proposed method can dramatically reduce the dimensionality of each band image, which makes it possible for band selection to be implemented in real time situations.     

结合倒置特征金字塔和U-Net的高光谱图像分类

目的 地物分类是对地观测研究领域的重要任务。高光谱图像具有丰富的地物光谱信息,可用于提升遥感图像地物分类的准确度。如何对高光谱图像进行有效的特征提取与表示是高光谱图像分类应用的关键问题。为此,本文提出了一种结合倒置特征金字塔和U-Net的高光谱图像分类方法。方法 对高光谱数据进行主成分分析（principal component analysis,PCA）降维,获取作为网络输入的重构图像数据,然后使用U-Net逐层提取高光谱重构图像的空间特征。与此同时,利用倒置的特征金字塔网络抽取相应层级的语义特征;通过特征融合,得到既有丰富的空间信息又有较强烈的语义响应的特征表示。提出的网络利用注意力机制在跳跃连接过程中实现对背景区域的特征响应抑制,最终实现了较高的地物分类精度。结果 分析了PCA降维方法和输入数据尺寸对分类性能的影响,并在Indian Pines、Pavia University、Salinas和Urban数据集上进行了对比实验。本文方法在4个数据集上分别取得了98.91%、99.85%、99.99%和87.43%的总体分类精度,与支持向量机（support vector machine,SVM）等相关算法相比,分类精度高出1%~15%。结论 本文提出一种结合倒置特征金字塔和U-Net的高光谱图像分类方法,可以应用于有限训练样本下的高光谱图像分类任务,并在多个数据集上取得了较高的分类精度。实验结果表明倒置特征金字塔结构与U-Net结合的算法能够高效地实现高光谱图像的特征提取与表示,从而获得更精细的分类结果。     

一种新的高光谱遥感图像降维方法

高光谱遥感图像的高数据维给图像进一步处理带来了困难，为了解决这一问题，提出了自适应波段选择(ABS)的降维方法。该方法充分考虑了高光谱图像的空间相关性和谱间相关性，通过计算各个波段的指数来选择信息量大并且与其他波段相关性小的波段。对各波段相应的指数重新排列之后，有两种方法来选择最终波段：一种是选择波段指数比设定指数大的波段，另一种方法是选择波段指数排在前n个的所有波段。为了验证ABS方法的有效性，对降维后的高光谱图像进行了贝叶斯监督分类，分类结果表明自适应波段选择的方法能够选择出信息丰富的波段，分类精度与使用原始波段相比提高10．4％，计算复杂度大大降低。     

一种改进的高光谱数据自适应波段选择方法

高光谱遥感数据具有的波段数目多、波段宽度窄、数据量庞大等特点给图像的进一步解译带来了困难。为了解决这一问题,对自适应波段选择的降维方法进行了改进,不仅考虑了高光谱遥感图像波段的信息量和波段间的相关性,更重要的是考虑了各地物连续光谱间的可分性。光谱间的可分性距离越大,表明类间的可分性越大,地物越清晰。首先选出了能有效区分图像上任意两类别的理想波段子集,再根据波段子集中任意3波段的相关系数之和最小和它们的均方差最小两个指标,选出任意两类对间那些包含信息量大、相关性又小、谱间差异又大的3波段组合(且不唯一),最后对整幅影像选出的最佳3波段45、75、85合成的假彩色图像用光谱角度制图法(SAM)进行了分类,总体分类精度达到91.7%,Kappa系数达到0.82。     

基于波段影像统计信息量加权K-means聚类的高光谱影像分类

针对分类过程中如何合理利用高光谱影像波段问题,提出一种基于波段影像统计量加权K-means聚类的高光谱影像分类算法.该算法的核心思想在于:由波段含有的信息量及波段间的相关性确定各波段权重,同时考虑各波段对各聚类的重要性.首先,根据波段影像的熵、标准差及均值定义波段信息量函数,根据相邻波段影像互信息定义相关性函数;其次,...     

Assessing the efficiency of multispectral satellite and airborne hyperspectral images for land cover mapping in an aquatic environment with emphasis on the water caltrop (Trapa natans)

A number of clear issues are pertinent when considering whether, or not, to use a remotely sensed dataset. We evaluate these issues here by comparing an aerial hyperspectral image at 1.5 m geometric resolution that comprises 128 narrow bands within a spectral range between 400 nm and 1,000 nm as well as a nine-band Landsat 8 image at 30.0 m geometric resolution. We therefore applied Random Forest (RF) and Support Vector Machine (SVM) classifiers utilizing different input data sets to determine the best thematic accuracy for both types of images by involving all possible bands and then minimized them using variable selection and dimension reduction via Minimum Noise Fraction (MNF). We then compared Landsat images to an aerial hyperspectral one. The results of this analysis revealed that band selections based on variable importance and MNF-transformation improved thematic accuracy assessed as Overall Accuracy (OA). Results reveal a 1.00% improvement in OA via variable selection as 59 bands instead of 128 bands and a 1.50% via MNF-transformation of the hyperspectral image. This improvement was 4.52% in the Landsat image when using a MNF-transformation compared to the best performances without transformation or variable selection. Data also showed that application of Landsat spectral range on hyperspectral bands resulted in different outcomes; specifically, SVM resulted in a 91.50% OA while RF resulted in 95.50% OA. Landscape ecology results show that use of the Landsat image provided fewer land cover patches and that differences encompassed 6.30% of the whole area. We therefore conclude that Landsat data can be used with a number of limitations for accurate ecological mapping.     

基于边缘保持滤波的高光谱影像光谱-空间联合分类

针对高光谱遥感影像分类过程中,高维数据引起的"维数灾难"以及空间邻域一致性信息没有得到充分利用的问题,提出一种基于边缘保持滤波（Edge-preserving filtering,EPF）的高光谱影像光谱-空间联合分类算法.该算法首先进行波段子集划分和主成分提取,构造新的低维特征集,在保存影像结构信息的前提下降低数据维度;其次利用支持向量机（Support vector machine,SVM）获得低维特征集的初始分类概率图;然后利用原始影像主成分对初始分类概率图进行边缘保持滤波,融合光谱信息和空间信息;最后根据滤波后分类概率图对应像素点值的大小确定每个像素的类别.在Indian Pines和Pavia University两组高光谱数据上进行仿真实验,相同实验条件下,本文算法都获得最高分类精度和最少的时间消耗.仿真结果表明本文算法在高光谱遥感影像分类任务中具有明显的优势.     

采用分段主成分和PPI的高光谱影像分类

高光谱遥感影像波段多且存在混合像元,特征提取以及端元提取都是高光谱影像分类必不可少的工作,分类方法的选择也是因地适宜。以福建省泉州市德化县下属某一地区的CASI影像为实验数据,基于分段主成分(segmental principal component analysis,SPCA)和纯净像元指数法(pure pixel index,PPI),提出了最小距离(minimum distance classification,MDC)和二进制编码(binary encoding,BE)的高光谱影像分类方法。实验结果表明,MDC的总体精度为69.71%,BE的总体精度为70.88%。对单一地物精度而言2种方法各有其长,MDC对道路的分类精度更高,为98.08%;而植被、耕地和水体采用BE方法的分类精度更高,分别为94.12%、98.08%、98.11%。本文提出的方法应用于CASI高光谱影像,对该研究区的地物分类研究有一定的实用性和参考价值。