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高光谱遥感技术从20世纪80年代出现以来,已迅速成为对地观测的重要组成部分,其影像信息提取是地物信息提取的主要数据来源。高光谱遥感影像除提供地物的空间信息之外,其成百上千个波段携带的光谱信息所提供的光谱诊断能力可以对地物目标进行精细化解译,大大增强了对地物信息的提取能力。充分利用高光谱遥感影像丰富的光谱信息对地物目标进行精细化解译成为近年来遥感领域的研究热点。对基于量子优化算法的高光谱遥感影像处理方法进行阐述,介绍了量子优化算法的发展与技术,并概括了其在高光谱遥感影像中的应用,并对量子优化算法在高光谱遥感影像处理中的应用发展提出建议和展望。 相似文献
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本文针对遥感影像复杂背景下,背景地物光谱特征与目标光谱特征之间存在较强相关性的问题,提出一种基于仿蝇视觉的复杂背景下遥感异常检测算法。首先构建并行多孔径背景模型,实现对复杂背景特征的自适应描述;然后基于异常目标的光谱特征相对异常性,采用相对马氏距离区分异常区域、不确定区域与无目标区域,消除背景与目标光谱相关性对检测结果干扰的同时,弥补了传统假设检验无法区分无目标和不确定问题的不足;最后融合多个背景模型的检测结果,实现异常目标检测。仿真实验将围绕多种背景地物并存复杂区域的异常检测验证本文算法的有效性。 相似文献
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随着航空航天技术与遥感技术的不断发展,遥感影像在诸多领域的应用不断拓展,其中高光谱分辨率遥感影像具有“图谱合一”的特点,即该数据既包含了具有强大区分性的地物光谱信息,又包含了丰富的地物空间位置信息,因此高光谱数据具有非常大的应用潜力。高光谱异常目标检测问题,是在对目标先验信息未知的前提下,根据光谱与空间信息实现对区域中的异常目标的进行“盲”检测,因此其在资源调查、灾害救援等领域发挥了巨大的作用,是遥感领域非常重要的研究课题。本文针对高光谱遥感影像异常目标检测研究方向,首先总结阐述了目前高光谱异常目标检测问题的主要研究进展,根据算法原理的不同对现有主流算法进行了分类与总结,主要分成了基于统计学、基于数据表达、基于数据分解、基于深度学习等不同的种类的方法,并对每类方法的特点进行分析。随后通过对现有方法的调研、分析与总结,提出了数据库拓展、多源数据融合、算法实用化等高光谱异常检测研究未来发展的3个方向。 相似文献
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复杂的背景信息和高维冗余波段是影响高光谱遥感影像异常目标检测精度的重要因素.本文针对高光谱影像异常目标提取提出了一种子空间分析孤立森林探测方法.该方法不对背景做高斯分布假设,通过正交子空间分析增强输入特征影像中潜在异常目标与背景之间的对比度,通过主成分分析法降维来降低孤立森林算法带来的不确定性,运用了全局和局部结合的思想实现异常目标检测.在停机坪、海滩、港口和草地4个不同场景的高光谱影像上的试验结果表明,本方法的异常目标提取精度较经典方法取得了更好的结果.该方法不仅有效地处理了高光谱遥感影像的复杂背景和高维问题,还有效地利用了空间信息. 相似文献
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针对高光谱影像数据中存在大量冗余,传统异常探测算法应用高光谱所有波段进行探测计算量巨大的问题,提出一种基于波段相似性线性预测与学习字典的异常探测算法。该算法首先通过对波段的相似性进行线性预测,找到最不相似的波段子集;然后,利用学习字典算法获得能够表征图像背景信息的背景字典,并通过低秩分解的算法将影像分解为低秩矩阵与稀疏矩阵;最后,使用经典RXD(Reed-X detector)探测算法对稀疏影像进行异常探测。实验结果表明,该算法可以在减少计算代价、保持波段原始信息不被破坏的同时,能够较好地实现了高光谱影像的异常探测。 相似文献
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常世桢 《武汉大学学报(信息科学版)》2022,47(1):158-158
高光谱遥感图像在采集过程中既获得了场景空间分布信息,又以近似连续的方式记录地物的光谱信息.高光谱目标探测研究正是利用了数据光谱分辨率高、细微特征表达精确的优势,根据不同地物间的诊断性信息进行探测.近些年,机器学习与优化分析理论的发展为高光谱图像处理增添新的活力.本文从高光谱遥感图像目标探测基本理论及发展难点切入,针对光... 相似文献
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以杂多县冰川为研究对象,为了精确提取冰川信息,根据冰川在TM影像上存在的光谱差异,将冰川划分成两类。分析较难与周边地物区分的冰舌与其他地物之间的光谱特征区分性,选择最优的冰川提取波段。同时利用波段运算得到对冰川信息提取更有利的波段。将所有优选波段经过最佳指数计算和地物可分离性分析,得到波段间相关性最低,冗余信息最少,冰川与其它地物可分性最强的波段组合进行监督分类,有效地分离出冰川。将本文方法提取的冰川面积与高分辨率影像中提取的冰川面积相比较,结果表明本文提出的冰川提取的技术方法是有效的。 相似文献
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Hyperspectral images (HSI) provide a new way to exploit the internal physical composition of the land scene. The basic platform for acquiring HSI data-sets are airborne or spaceborne spectral imaging. Retrieving useful information from hyperspectral images can be grouped into four categories. (1) Classification: Hyperspectral images provide so much spectral and spatial information that remotely sensed image classification has become a complex task. (2) Endmember extraction and spectral unmixing: Among images, only HSI have a complete model to represent the internal structure of each pixel where the endmembers are the elements. Identification of endmembers from HSI thus becomes the foremost step in interpretation of each pixel. With proper endmembers, the corresponding abundances can also be exactly calculated. (3) Target detection: Another practical problem is how to determine the existence of certain resolved or full pixel objects from a complex background. Constructing a reliable rule for separating target signals from all the other background signals, even in the case of low target occurrence and high spectral variation, comprises the key to this problem. (4) Change detection: Although change detection is not a new problem, detecting changes from hyperspectral images has brought new challenges, since the spectral bands are so many, accurate band-to-band correspondences and minor changes in subclass land objects can be depicted in HSI. In this paper, the basic theory and the most canonical works are discussed, along with the most recent advances in each aspect of hyperspectral image processing. 相似文献
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Maryam Imani 《Journal of the Indian Society of Remote Sensing》2018,46(9):1389-1397
An anomaly detection method with a clustering based feature reduction is proposed in this paper to improve the performance of the Local RX detector. Because of high dimensionality of hyperspectral image and the low number of available samples in each local region around each testing pixel, the estimate of local covariance matrix is not possible. So, because of singularity problem, Local RX cannot use the local covariance matrix and misses the local structures of data to model the background clutter. To deal with this problem, a supervised clustering based feature reduction is introduced for extraction of background features with minimum overlap and redundant information. In the projected feature space with reduced dimensionality, the local structures of background pixels are estimated to efficiently model the background data. The experiments done on both synthetic and real hyperspectral images show the superior detection performance of the proposed method with a relatively high speed. 相似文献
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This paper discusses a statistical and band transformation based approach to select bands for hyperspectral image analysis. Hyperspectral images contain large number of spectral bands with redundant information about the spectral classes in the image scene. It is necessary to reduce the high dimensionality of the data for the processing of hyperspectral data. We report a feature selection technique that removes correlated spectral bands using band decorrelation technique and obtains maximum variance image bands based on factor analysis. Factor analysis method of band selection technique is also validated against existing methods of band selection. The study is carried out for the agriculturally rich area of Musiri region of South India that has varied landcover types. Evaluation of the band selection procedure is done using signature separability measures such as Euclidean distance, Divergence, Transformed divergence and Jeffries Matusita distance. Results indicated that selected bands exhibited maximum separability and also occurred predominantly at wavelength 700 nm, 850, 1000 nm, 1200 nm, 1648 nm and 2200 nm. 相似文献
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Fractal-based dimensionality reduction of hyperspectral images 总被引：3，自引：0，他引：3
Jayanta Kumar Ghosh Ankur Somvanshi 《Journal of the Indian Society of Remote Sensing》2008,36(3):235-241
The spectral reflectance of any pixel in a remote sensing image depends on the characteristics of the particular land cover
(LC) present in the Instantaneous Field of View (IFOV) of the sensor. The fractal dimension of the spectral reflectance curve
(SRC) of any pixel can thus be visualized as a representation of the characteristics of the LC. Based on this, a fractalbased
method for reduction of the dimensionality of Hyperspectral (HS) images has been investigated. The fractal dimension (FD)
of SRC has been calculated by adopting a method based on Hausdorff metric that reduces the dimensionality from N HS bands
to a single feature incorporating the characteristics associated with each of the bands. Further, it has been established
that FD values can be used as a feature to identify anomaly in water cover. 相似文献
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A. Senthil Kumar V. Keerthi A.S. Manjunath Harald van der Werff Freek van der Meer 《International Journal of Applied Earth Observation and Geoinformation》2010
Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously. 相似文献
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T. Ch. Malleswara Rao G. Jai Sankar T. Roopesh Kumar 《Journal of the Indian Society of Remote Sensing》2012,40(2):191-200
The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems
of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification
accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large
multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection
of a flexible classifier with robust learning process. Experiments conducted revealed that a computationally cheap algorithm
that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in
helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for
segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions.
The proposed method’s overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging
Spectroradiometer Indian Pines hyperspectral benchmark data set and presented. 相似文献
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A Selective KPCA Algorithm Based on High-Order Statistics for Anomaly Detection in Hyperspectral Imagery 总被引：1，自引：0，他引：1
《Geoscience and Remote Sensing Letters, IEEE》2008,5(1):43-47
In this letter, a selective kernel principal component analysis (KPCA) algorithm based on high-order statistics is proposed for anomaly detection in hyperspectral imagery. First, KPCA is performed on the original hyperspectral data to fully mine the high-order correlation between spectral bands. Then, the average local singularity (LS) is defined based on the high-order statistics in the local sliding window, which is used as a measure for selecting the most informative nonlinear component for anomaly detection. By the selective KPCA, information on anomalous targets is extracted to maximum extent, and background clutters are well suppressed in the selected component. Finally, the selected component with maximum average LS is used as input for anomaly detectors. Numerical experiments are conducted on real hyperspectral images collected by the airborne visible/infrared imaging spectrometer. The results strongly prove the effectiveness of the proposed algorithm. 相似文献
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高光谱成像技术具有光谱连续、图谱合一,能够以较高的光谱诊断能力对地物目标进行精细化解译,可以大幅增强地物信息的提取能力。充分利用高光谱遥感图像丰富的空间、谱信息,进行观测目标地物的精细化解译,成为近年来遥感领域的研究热点和前沿领域,并在多个相关领域具有巨大的应用价值和广阔的发展前景。本文结合高光谱图像成像特点,对基于稀疏表示理论的高光谱图像处理与分析方法进行综述,概括了高光谱图像处理与分析主要研究,并对各个研究领域与方向进行分析和评价,最后对各研究领域发展提出建议和展望。 相似文献
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针对高光谱遥感异常检测中复杂背景与异常目标之间光谱特征相关性导致背景模型难以准确估计的问题,提出了一种基于多孔径映射的高光谱遥感异常检测算法。首先,不同于背景建模提取背景特征的方法,多孔径映射从不同角度提取数据特征,通过构建基集合表征高光谱数据的光谱特性,获得用于衡量统计差异的异常显著性指标。其次,为了实现对具有适中及低异常显著性像素的精细分析,本文基于模糊逻辑理论构建隶属度函数获得关于像素异常显著性的连续性属性标记,并将隶属度值作为权重,通过加权迭代过程实现多孔径映射的自适应收敛。最后,借鉴模糊逻辑理论中的去模糊机制,对多孔径检测结果进行融合,获得最终的检测结果。本文仿真试验采用高光谱遥感数据,从稳健性及对低显著度目标敏感性方面对算法进行验证。 相似文献