Visual method for spectral band selection

We present a new method for performing band selection experiments with spectral data. This method allows for the visual inspection and assessment of the experiment results, and includes a statistical significance test. The method follows a standard feature selection approach in which a multivariate distance measure is used as a figure of merit in a search-optimization procedure. For this letter, we have chosen the Jeffries-Matusita distance between each sample and its immediate background. The band selection methodology uses either an exhaustive search over all possible combinations of 1-4 bands or sequential forward selection. To analyze the band selection results, we count the number of times that each band is selected as a member of the best set by the protocol, and we plot the results as a band frequency histogram. This allows us to visually discern spectral patterns that are not evident otherwise, and thus better assess the utility of each spectral band. We can compute band frequency histograms over individual classes of samples or over groups of classes. In addition, we can compute a significance statistic that gives us the probability that a given histogram is not the result of random band selection outcomes.     

鲁棒多特征谱聚类的高光谱影像波段选择

传统谱聚类的高光谱影像波段选择模型中,采用的波段相似矩阵受到噪声或异常值的影响且仅能表征波段的单一相似特征,导致波段子集的选取结果受到限制.本文从波段选择的目的 出发,提出鲁棒多特征谱聚类方法,整合多个特征的波段相似矩阵来形成综合相似矩阵以解决上述问题.该方法假设4种相似性度量包括光谱信息散度、光谱角度距离、波段相关性...     

Incorporating band selection in the spatial selection of spectral endmembers

The impact of band selection on endmember selection is seldom explored in the analysis of hyperspectral imagery. This study incorporates the N-dimensional Spectral Solid Angle (NSSA) band selection tool into the Spectral-Spatial Endmember Extraction (SSEE) tool to determine a band set that can be used to better define endmembers classes used in spectral mixture analysis. The incorporation aims to define a band set that improves the spectral contrast between endmembers at each step of the spatial-spectral endmember search and ultimately captures key features for discriminating spectrally similar materials. The proposed method (NSSA-SSEE) was evaluated for lithological mapping using a hyperspectral image encompassing a range of spectrally similar mafic and ultramafic rock units. The band selected by NSSA-SSEE showed a good agreement with known features of scene components identified by experts. Results showed an improvement in the selection of detailed endmembers, endmembers that are similar and that can be significant for mapping. The incorporation of NSSA into SSEE was feasible because both methods are well suited for this process. NSSA is one of the few methods of band selection that is suitable for the analysis of a small number of endmembers and SSEE provides such endmember sets via spatial subsetting. The automated NSSA-SSEE approach can reduce the need for field-based information to guide the feature selection process.     

面向土地利用分类的HJ-1 CCD影像最佳分形波段选择

环境一号卫星（HJ-1）CCD影像光谱波段较少,地物之间的准确分类识别有一定困难。采用分形纹理辅助地物分类识别是一种有效方法,而波段选择是提高分类识别精度的关键。本文以江西赣州定南县土地利用分类为例,采用双毯覆盖模型对HJ卫星CCD影像6类典型地物的波谱分形特征进行了分析,利用不同地物在不同波段上的分形区分度差异构建了最佳分形波段选择模型,并利用该模型挑选出最佳分形波段来辅助土地利用分类,最后对分类结果进行检验。结果表明：最佳分形波段选择模型能够综合权衡不同地物在不同波段上的分形区分度差异,利用挑选出来的最佳分形波段来辅助分类,其分类总体精度相对于原始影像分类提高了11.77%,相对于第1主成分分形辅助下的分类提高了1.56%。     

高光谱图像分类的Wasserstein配置熵非监督波段选择方法

高光谱图像波段选择需考虑波段信息.传统香农信息熵指标仅考虑图像的组分信息(像元的种类和比例),忽略了图像的空间配置信息(像元的空间分布),后者可由玻尔兹曼熵刻画.其中,Wasserstein配置熵删除了连续像元的冗余信息,但局限于四邻域,本文将Wasserstein配置熵拓展至八邻域.以印度松木试验场和意大利帕维亚大学...     

一种基于地物波谱特征的最佳波段组合选取方法

对多光谱数据选取最佳的波段组合,是图像解译和专题信息提取的重要前提。文中提出一种基于地物波谱特征的最佳波段组合选取方法,即综合考虑方差、相关系数、OIF指数和地物间的可分离性4个因素,利用ERDAS和EXCEL等工具进行各指标的解算,并通过实验,选择红菱矿区1995年的TM多光谱影像为数据源,选取了基于水体波谱特征的最佳波段组合TM345。经定性分析和定量计算,验证了该方法的可行性。     

高光谱图像子空间的波段选择

为降低高光谱遥感数据光谱空间的冗余度,提出一种快速的波段选择方法。该方法在波段子空间下进行,依次选择各子空间中方差最大的波段作为初始波段,设定目标函数,然后逐子空间替换波段使得目标性能更加优化,直至没有替换可以使得目标更优为止。在两个公开高光谱影像数据集上对比3种常用波段选择方法(ABC、AP、ABS)来验证提出方法的有效性,实验结果表明:(1)在印第安纳数据上,本文方法与ABC、AP、ABS所选波段子集相比平均相关性分别降低22.04%、52.61%、55.71%,最佳指数分别提高0.58%、51.73%、0.95%,总体分类精度分别提高0.16%、1.39%、23.07%,在搜索效率上与同类型的ABC方法相比提高6.61%—69.02%;(2)在帕维亚大学数据上,本文方法与ABC、AP、ABS所选波段子集相比平均相关性分别降低2.38%、0.51%、32.83%,最佳指数分别提高1.34%、17.97%、12.92%,总体分类精度分别提高0.31%、0.69%、8.53%,在搜索效率上与同类型的ABC方法相比提高19.13%—86.34%。本文提出的波段选择方法能够选择合适的波段子集满足不同的应用需要,是一种有效的波段选择方法。     

A method to compute the n-dimensional solid spectral angle between vectors and its use for band selection in hyperspectral data

This study presents the calculation of spectral angle beyond two endmember vectors to the n-dimensional solid spectral angle (NSSA). The calculation of the NSSA is used to characterize the local spectral shape difference among a set of endmembers, leading to a methodology for band selection based on spectral shape variations of more than two spectra. Equidistributed sequences used in the quasi-Monte Carlo method (ESMC) for numerical simulations are shown to expedite the calculation of the NSSA. We develop a band selection method using the computation of NSSA(ϑ_n) in the context of a sliding window. By sliding the window over all bands available for varying band intervals, the calculated solid spectral angle values can capture the similarity of the endmembers over all spectral regions available and for spectral features of varying widths. By selecting a subset of spectral bands with largest solid spectral angles, a methodology can be developed to capture the most important spectral information for the separation or mapping of endmembers. We provide an example of the merits of the NSSA-ESMC method for band selection as applied to linear spectral unmixing. Specifically, we examine the endmember abundance errors resulting from the NSSA band selection method as opposed to using the full spectral dimensionality available.     

A semilabeled-sample-driven bagging technique for ill-posed classification problems

In this letter, a semilabeled-sample-driven bootstrap aggregating (bagging) technique based on a co-inference (inductive and transductive) framework is proposed for addressing ill-posed classification problems. The novelties of the proposed technique lie in: 1) the definition of a general classification strategy for ill-posed problems by the joint use of training and semilabeled samples (i.e., original unlabeled samples labeled by the classification process); and 2) the design of an effective bagging method (driven by semilabeled samples) for a proper exploitation of different classifiers based on bootstrapped hybrid training sets. Although the proposed technique is general and can be applied to any classification algorithm, in this letter multilayer perceptron neural networks (MLPs) are used to develop the basic classifier of the proposed architecture. In this context, a novel cost function for the training of MLPs is defined, which properly considers the contribution of semilabeled samples in the learning of each member of the ensemble. The experimental results, which are obtained on different ill-posed classification problems, confirm the effectiveness of the proposed technique.     

Genetic feature selection for texture classification

This paper presents a novel approach to feature subset selection using genetic algorithms. This approach has the ability to accommodate multiple criteria such as the accuracy and cost of classification into the process of feature selection and finds the effective feature subset for texture classification. On the basis of the effective feature subset selected, a method is described to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The methodology presented in this paper is illustrated by its application to the problem of trees extraction from aerial images.     

Genetic feature selection for texture classification

This paper presents a novel approach to feature subset selection using genetic algorithms. This approach has the ability to accommodate multiple criteria such as the accuracy and cost of classification into the process of feature selection and finds the effective feature subset for texture classification. On the basis of the effective feature subset selected, a method is described to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The methodology presented in this paper is illustrated by its application to the problem of trees extraction from aerial images.     

基于PCA的高光谱遥感图像分类

高光谱遥感图像的出现进一步提升遥感图像分类的准确性,但高光谱遥感图像的数据量大,处理高光谱遥感图像复杂度高、效率低。为解决这一问题,将主成分分析算法作为遥感图像分类的预处理技术。分析主成分分析算法的原理,利用主成分分析算法提取高光谱图像的主要波段图像。通过实验验证得出结论:高光谱遥感图像的主波段图像包含分类所需的大部分信息,利用少数的主波段图像即可达到70%以上的分类正确率。实验结果表明,在保证分类正确率的前提下,PCA算法可有效地减少图像分类处理的数据量,提高图像的处理效率。     

A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery

Among various image fusion methods, intensity-hue-saturation (IHS) technique is capable of quickly merging the massive volumes of data. For IKONOS imagery, IHS can yield satisfactory "spatial" enhancement but may introduce "spectral" distortion, appearing as a change in colors between compositions of resampled and fused multispectral bands. To solve this problem, a fast IHS fusion technique with spectral adjustment is presented. The experimental results demonstrate that the proposed approach can provide better performance than the original IHS method, both in processing speed and image quality.     

Selection of spectral band combination for land cover/land use classification using a brightness value overlapping index (BVOI)

A simply defined, accurate and efficient criterion of selecting a spectral-band combination for improved land use/land cover classification using remote sensing data is discussed. Results indicate that Brightness Value Overlapping Index (BVOI) is very effective in measuring the degree of overlap in brightness values among land cover types and in selecting suitable spectral-band combination for landuse classification. The results of BVOI are also compared with the results of another band-combination selecting index - Optimum Index Factor (OIF).     

TM classification using local spectral variability

A classification method which takes into account not only spectral but also spatial features for LANDSAT‐4 and 5 Thematic Mapper (TM) data is proposed. In accordance with improvement of Instantaneous Field of View (IFOV), spatial information such as textural, contextual, etc. is also increased so that some treatments of such information is highly required. One of the simplest spatial features is local spectral variability such as standard deviation, variability constant, variance, etc. in small cells such as 2x2,3x3 pixels. Such information can be used together with conventional spectral features in an unified way, for the traditional classifier such as a pixel‐wise Maximum Likelihood Decision Rule (MLDR). From the experiments, there was a substantial improvement in overall classification accuracy for TM forestry data. The probability of correct classification (PCC) for the new clearcut and the alpine meadow classes increased by 7% to 97% correct. The confusion between alpine meadow and new clearcut was reduced from 9% to 3%.     

An effective ensemble classification framework using random forests and a correlation based feature selection technique

Accurate classification of heterogeneous land surfaces with homogeneous land cover classes is a challenging task as satellite images are characterized by a large number of features in the spectral and spatial domains. The identifying relevance of a feature or feature set is an important task for designing an effective classification scheme. Here, an ensemble of random forests (RF) classifiers is realized on the basis of relevance of features. Correlation‐based Feature Selection (CFS) was utilized to assess the relevance of a subset of features by studying the individual predictive ability of each feature along with the degree of redundancy between them. Predictability of RF was greatly improved by random selection of the relevant features in each of the splits. An investigation was carried out on different types of images from the Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and QuickBird sensors. It has been observed that the performance of the RF classifier was significantly improved while using the optimal set of relevant features compared with a few of the most advanced supervised classifiers such as maximum likelihood classifier (MLC), Navie Bayes, multi‐layer perception (MLP), support vector machine (SVM) and bagging.     

A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images

An advanced context-sensitive classification technique that exploits a temporal series of remote sensing images for a regular updating of land-cover maps is proposed. This technique extends the use of spatio-contextual information to the framework of partially supervised approaches (that are capable of addressing the updating problem under the realistic, though critical, constraint that no ground-truth information is available for some of the images to be classified). The proposed classifier is based on an iterative partially supervised algorithm that jointly estimates the class-conditional densities and the prior model for the class labels on the image to be classified by taking into account spatio-contextual information. Experimental results point out that the proposed technique is effective and that it significantly outperforms the context-insensitive partially supervised approaches presented in the literature.     

高光谱图像波段选择的改进二进制布谷鸟算法

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。     

结合光谱角的最大似然法遥感影像分类

遥感影像含有丰富的信息,反映了地物特征。其中光谱角侧重描述了光谱的形状特征,具有对多光谱图像增益不敏感的特点。最大似然法是遥感影像分类最常用的方法之一,文中对该方法的后验概率判别函数进行修改,将光谱角以概率因子的形式加入到判别函数中构造一种新的判别函数,有机地将光谱角这一特征信息加入影像分类。通过实验,并与最大似然法和光谱角匹配法分类结果进行比较,结果表明,结合光谱角的最大似然分类法的分类精度得到提高。