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Data from abnormal channels in an imaging spectrometer almost always exerts an undesired impact on spectrum matching, classification, pattern recognition and other applications in hyperspectral remote sensing. To solve this problem, researchers should get rid of the data acquired by these channels. Selecting abnormal channels just in the way of visually examining each band image in a imaging data set is a conceivably hard and boring job. To relieve the burden, this paper proposes a method which exploits the spatial and spectral autocorrelations inherent in imaging spectrometer data, and can be used to speed up and, to a great degree, automate the detection of abnormal channels in an imaging spectrometer. This method is applied easily and successfully to one PHI data set and one Hymap data set, and can be applied to remotely sensed data from other hyperspectral sensors. 相似文献
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A margin-based feature selection approach is explored for hyperspectral data. This approach is based on measuring the confidence of a classifier when making predictions on a test data. Greedy feature flip and iterative search algorithms, which attempts to maximise the margin-based evaluation functions, were used in the present study. Evaluation functions use linear, zero–one and sigmoid utility functions where a utility function controls the contribution of each margin term to the overall score. The results obtained by margin-based feature selection technique were compared to a support vector machine-based recurring feature elimination approach. Two different hyperspectral data sets, one consisting of 65 bands (DAIS data) and other with 185 bands (AVIRIS data) were used. With digital airborne imaging spectrometer (DAIS) data, the classification accuracy by greedy feature flip algorithm and sigmoid utility function was 93.02% using a total of 24 selected features in comparison to an accuracy of 91.76% with full set of 65 features. The results suggest a significant increase in classification accuracy with 24 selected features. The classification accuracy (93.4%) achieved by the iterative search margin-based algorithm with 20 selected features using sigmoid utility function is also significantly more accurate than that achieved with 65 features. To judge the usefulness of margin-based feature selection approaches, another hyperspectral data set consisting of 185 features was used. A total of 65 selected features were used to evaluate the performance of margin-based feature selection approach. The results suggest a significantly improved performance by greedy feature flip-based feature selection technique with this data set also. This study also suggest that margin-based feature selection algorithms provide a comparable performance to support vector machine-based recurring feature elimination approach. 相似文献
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高光谱遥感影像具有丰富的光谱信息,在地物分类识别方面具有明显的优势。针对复杂高光谱影像分类问题,应用了一种广义判别分析特征提取技术。将输入样本通过非线性函数映射到特征空间,在特征空间中应用线性判别特征提取方法;算法求解过程中涉及到在特征空间的内积用核函数代替,简化计算的同时也使得算法与非线性函数的具体形式无关。通过影像分类试验表明,该方法较常用特征提取方法更有利于分类精度的提高。 相似文献
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受仪器和观测条件限制,高光谱数据易受噪声污染,给数据解译带来挑战。针对传统稀疏解混模型抗噪性能差的问题,本文提出一种截断加权核范数稀疏解混方法,利用高光谱图像像元之间的相关性减轻噪声对丰度估计的干扰。该方法借助低秩表示在挖掘数据内在低维结构方面的优势,在稀疏解混中加入基于截断加权核范数的低秩约束,并结合加权稀疏技术,在稀疏正则项中引入空间邻域权重。截断加权核范数对丰度矩阵的奇异值向量分段处理,可以更好地实现丰度矩阵的低秩逼近,使丰度图像保持空间一致性并保留更多细节信息,空间加权策略则增强了丰度图像的空间连续性。模拟高光谱数据、Cuprite矿区真实数据和红树林高光谱数据实验表明,与其他先进的稀疏解混方法相比,所提方法具有更好的抗噪性,能够提高解混精度。 相似文献
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Lian-Ru Gao Bing Zhang Xia Zhang Wen-Juan Zhang Qing-Xi Tong 《Geoscience and Remote Sensing Letters, IEEE》2008,5(1):83-87
A new method for estimating noise in hyperspectral images is described in this letter. Our method is based on the general internal regularity of Earth objects and the strong spectral correlation of hyperspectral images. It can be used to automatically estimate noise for both radiance and reflectance images. Unlike other methods discussed in this letter, our method is more reliable and adaptable, which we demonstrate using simulated images with different scene contents. Finally, we successfully applied this new method in estimating noise for pushbroom hyperspectral imager (PHI) data. 相似文献
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受成像光谱仪性能与复杂地物分布的影响,高光谱图像存在大量的混合像元。传统的基于学习的混合像元分解方法通常都是浅层模型,或缺少对空间、光谱信息的综合应用。本文提出一种多维卷积网络协同的混合像元分解深层模型,采用多种维度卷积网络能更充分利用多种维度语义信息,有利于估计小样本和高维的高光谱图像混合像元丰度。对训练数据进行增广处理,构建光谱维、空间维和立方体3种卷积神经网络;设计了融合层,协同3种卷积神经网络提取特征,“端到端”的估计混合像元丰度值;模型使用了批量归一化、池化和Dropout方法避免过拟合现象。试验结果表明,多维卷积网络协同方法的引入能更有效地提取空-谱特征信息,与其他的卷积网络解混模型相比,估计的混合像元丰度精度有显著提高。 相似文献
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天宫一号高光谱成像仪是目前中国空间分辨率和光谱综合指标最高的星载光谱成像仪.针对天宫一号高光谱数据,提出了一种基于波段相关检测的蚀变信息提取方法.该方法在对天宫一号高光谱数据进行波段相关检测提取蚀变异常特征波段的基础上,结合异常探测、端元提取和光谱匹配等方法的各自优势进行天高一号高光谱数据的蚀变信息提取.利用该法成功提取了天宫一号高光谱数据中的Al-OH矿物蚀变异常及4种蚀变矿物,结果表明该方法可以有效提取天宫一号高光谱数据中的蚀变信息,有助于天宫一号高光谱数据在地质矿产资源调查中发挥巨大的应用潜力. 相似文献
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基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型 总被引：19，自引：1，他引：19
旨在寻找叶绿素a的高光谱遥感敏感波段并建立其定量估算模型。通过对太湖水体的连续监测，获得了从2004年6月到8月3个月的太湖水体高光谱数据和水质化学分析数据。利用实测的高光谱数据分析计算太湖水体的离水辐亮度和遥感反射比；然后，通过相关分析寻找反演叶绿素a浓度的高光谱敏感波段，进而建立反演太湖水体叶绿素a浓度的高光谱遥感定量估算模型，并用相关数据对模型进行精度分析。研究发现，水体的遥感反射比光谱在719nm和725nm存在两个峰，其中719nm处的峰更明显且稳定。通过模型的对比分析，发现用这两个峰值处的遥感反射比参与建模可以提高叶绿素a的估算精度；并且认为由反射比比值变量R719／R670所建立的线性模型对叶绿素a浓度的估算精度最理想。 相似文献
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基于粗糙集和遗传算法的超光谱波段集合整体缩减 总被引：3，自引：0，他引：3
提出一种对高光谱遥感影像波段集合进行整体缩减的方法。该方法首先根据模糊集和粗糙集理论,对原光谱波段集会进行近似等价波段区间的自动划分。对于每个近似等价波段区间,在考虑其他区间合成影像数据影响的情况下,利用遗传算法进行合成权系数的优化。实验表明,整体迭代线性合成方法不仅具有较高的计算效率,而且可以获得比独立线性合成方法明显优化的结果。 相似文献
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遥感探测到的小目标信号一般是弱信号,利用传统的高光谱异常变化检测方法直接抑制背景来突出异常变化目标,往往导致小目标弱信号同时被抑制,造成目标探测率低、虚警率高。基于独立成分分析方法,研究了弱信号小目标的高光谱变化检测模型,该模型首先通过投影寻踪将异常变化影像投影到独立成分,突出异常变化目标,然后再抑制背景,从而达到异常变化目标和背景的有效分离。该模型可以有效降低虚警率,提高探测率。利用模拟数据和真实数据进行了精度验证,结果表明,利用模拟数据得到的探测精度为99%,利用真实数据得到的检测精度为86%,与传统异常变化检测算法相比,精度最高提高了9%。本文研究方法适用于弱信号小目标的高光谱异常变化检测。 相似文献
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Francesco Dell’Endice Jens Nieke Benjamin Koetz Michael E. Schaepman Klaus Itten 《ISPRS Journal of Photogrammetry and Remote Sensing》2009,64(6):632-639
Programmable imaging spectrometers can be adjusted to fit specific application requirements that differ from the instrument initial spectral design goals. Sensor spectral characteristics and its signal-to-noise ratio (SNR) can be changed by applying customized online binning patterns.We present a software utility that generates application driven spectral binning patterns by using an SNR dependent sensor model. The utility, named BinGO (BInning patterN Generator and Optimiser), is used to produce predefined binning patterns that either (a) allow an existing imaging spectrometer to optimize its spectral characteristics for a specific application, (b) allow an existing imaging spectrometer to spectral and/or spatially emulate another instrument, or (c) design new multispectral or imaging spectrometer missions (i.e. spaceborne, airborne, terrestrial). We present a variety of BinGO case studies, including the simulation of airborne (APEX) [Itten, K.I. et al., 2008. APEX — The hyperspectral ESA Airborne Prism Experiment. Sensors 8(1), 1–25], spaceborne (SENTINEL III) [Nieke, J., Frerick, J., Stroede, J., Mavrocordatos, C., Berruti, B., 2008. Status of the optical payload and processor development of ESA’s Sentinel 3 mission. In: Proceedings of the Geoscience and Remote Sensing Symposium IGARSS 2008, pp. 427–430], as well as scientific and performance optimized approaches. We conclude that the presented approach can successfully be used to increase the efficiency of spectral information retrieval by using imaging spectroscopy data and to simulate various missions and requirements, finally supporting proper trade-off decisions to be made between performance optimization and scientific requirements. In addition, if specific sensor parameters are known, BinGO can also model other imaging spectrometers. 相似文献
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高光谱压缩感知(HCS)对于解决机载或星载高光谱数据的存储与实时传输具有重要意义。目前,线性混合模型(LMM)已被成功应用于HCS;然而,由于光照条件、地形变化以及大气作用等的影响,所获取的地物光谱会发生扰动,从而限制了HCS重建质量的提高。在LMM基础上,通过引入光谱修正项来修正光谱扰动,提出了光谱扰动修正的LMM (SPC_LMM);在此基础上,进一步提出了基于SPC_LMM的HCS (HCS_SPC_LMM)方法。该方法在采样端仅对原始高光谱图像进行光谱维压缩采样,基于压缩采样数据,将SPC_LMM应用HCS的重建,利用交替方向乘子法(ADMM)分别估计SPC_LMM中各分量的最优值,以获得最优的高光谱图像重建质量。实验结果表明,HCS_SPC_LMM能够获得优于其他典型HCS方法的重建质量。 相似文献
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成像高光谱数据在赤潮检测和识别中的应用研究 总被引：11，自引：1，他引：11
近年来,频繁发生的海洋赤潮灾害给我国沿海经济带来了巨大损失.在对海洋赤潮的监视监测中,航空遥感是重要的监测平台,推帚式成像光谱仪(PHI)是获取高分辨率高光谱图像的重要手段.2001年7月至8月,中国海监飞机对辽东湾海域频繁发生的大面积赤潮进行了航空遥感监测,首次获取了海上高光谱数据.本文以获取的高光谱数据为研究对象,通过合成假彩色图像、提取异常区域的反射率曲线以及构造相关分析函数等方法,初步探讨了利用高光谱数据进行赤潮水体检测和赤潮种类识别的方法. 相似文献
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基于密度的抽样和动态时间扭曲距离,提出了一种半监督高光谱模糊聚类方法。该方法首先应用基于密度的方法对样本进行抽样,然后采用动态时间扭曲距离计算样本之间的相似度,最后利用半监督模糊C均值算法进行聚类。为了验证所提出方法的有效性,在广泛使用的Indian Pines数据集和PaviaU数据集上进行试验。结果表明,本文提出的方法能够取得理想的分类结果。 相似文献
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Imaging spectroscopy is an emerging and versatile technique that finds applications in diverse fields concerned with remote identification, discrimination and mapping of materials. The large amount of spectral data produced by hyperspectral imaging necessitates the development of automated techniques that convert imagery directly into thematic maps. Spectral library search method, a method of choice for organic compound identification by the mass spectroscopy, has caught the attention of researchers as one of the appropriate methods for an efficient exploitation of high quality spectral data available from the hyperspectral imaging systems. Given the apparent increase in the number of papers appearing on the subject as well as the variety of methods proposed, it is reasonable to say that the field of automated interpretation of reflectance spectral data has passed its infancy now gaining important space in the scientific community. We present an overall view of the literature relevant to the development of library search method, the various search algorithms and systems available in the purview for developing an automated hyperspectral data analysis system for material identification. 相似文献