New independent component analysis method using higher order statistics with application to remote sensing images

Zhang, Xiaohui; Chen, Chi Hau

doi:10.1117/1.1482722

Cited by 31 publications

(11 citation statements)

References 15 publications

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“…Several authors have investigated the application of ICA methods to the analysis of remote sensing multispectral images [38]- [41]. ICA is defined as representing the pixel spectra by the linear combination of statistically independent components; since two classes are considered, two components were calculated for ICA.…”

Section: Methodsmentioning

confidence: 99%

A Spatial Contextual Postclassification Method for Preserving Linear Objects in Multispectral Imagery

Rodríguez-Cuenca

Malpica

Alonso

2013

IEEE Trans. Geosci. Remote Sensing

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Abstract-Classification of remote sensing multispectral data is important for segmenting images and thematic mapping and is generally the first step in feature extraction. Per-pixel classification, based on spectral information alone, generally produces noisy classification results. The introduction of spatial information has been shown to be beneficial in removing most of this noise. Probabilistic label relaxation (PLR) has proved to be advantageous using second-order statistics; here, we present a modified contextual probabilistic relaxation method based on imposing directional information in the joint probability with third-order statistics. The proposed method was tested in synthetic images and real images; the results are compared with a "Majority" algorithm and the classical PLR method. The proposed third-order method gives the best results, both visually and numerically.

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Section: Methodsmentioning

confidence: 99%

A Spatial Contextual Postclassification Method for Preserving Linear Objects in Multispectral Imagery

Rodríguez-Cuenca

Malpica

Alonso

2013

IEEE Trans. Geosci. Remote Sensing

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“…Similar to the PCA, the independent component analysis (ICA) [22] is a more recent statistical and computational technique born out of the fields of signal processing and neural computing. The ICA linearly transforms data into components that are maximally independent from each other [23,24], and is thus capable of identifying the underlying factors in a dataset that are a mixture of several sources, called independent components (IC). As a result, the technique can serve as a powerful unsupervised image classifier [23,25] that makes use of all data present in an image stack to produce comprehensive, unbiased class definitions [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Independent Component Analysis and Landsat 8 for Delineating Maximum Extent of the 2013 Colorado Front Range Flood

et al. 2015

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Maximum flood extent-a key data need for disaster response and mitigation-is rarely quantified due to storm-related cloud cover and the low temporal resolution of optical sensors. While change detection approaches can circumvent these issues through the identification of inundated land and soil from post-flood imagery, their accuracy can suffer in the narrow and complex channels of increasingly developed and heterogeneous floodplains. This study explored the utility of the Operational Land Imager (OLI) and Independent Component Analysis (ICA) for addressing these challenges in the unprecedented 2013 Flood along the Colorado Front Range, USA. Pre-and post-flood images were composited and transformed with an ICA to identify change classes. Flooded pixels were extracted using image segmentation, and the resulting flood layer was refined with cloud and irrigated agricultural masks derived from the ICA. Visual assessment against aerial orthophotography showed close agreement with high water marks and scoured riverbanks, and a pixel-to-pixel validation with WorldView-2 imagery captured near peak OPEN ACCESSRemote Sens. 2015, 7 9823 flow yielded an overall accuracy of 87% and Kappa of 0.73. Additional tests showed a twofold increase in flood class accuracy over the commonly used modified normalized water index. The approach was able to simultaneously distinguish flood-related water and soil moisture from pre-existing water bodies and other spectrally similar classes within the narrow and braided channels of the study site. This was accomplished without the use of post-processing smoothing operations, enabling the important preservation of nuanced inundation patterns. Although flooding beneath moderate and sparse riparian vegetation canopy was captured, dense vegetation cover and paved regions of the floodplain were main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the flood edge. Nevertheless, the unsupervised nature of ICA, in conjunction with the global availability of Landsat imagery, offers a straightforward, robust, and flexible approach to flood mapping that requires no ancillary data for rapid implementation. Finally, the spatial layer of flood extent and a summary of impacts were provided for use in the region's ongoing hydrologic research and mitigation planning.

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“…The results of the unmixing are often assessed visually by recognizing landmarks in the original image and in the unmixed data [11,14]. In some cases ground truth data are available and can be compared with the unmixing results [10,20].…”

Section: Introductionmentioning

confidence: 99%

“…This approach attempts to unmix the data by finding maximally independent abundances. A variety of ICA algorithms have been applied to hyperspectral unmixing including contextual ICA [10], joint cumulant-based ICA [11], joint approximate diagonalization of eigenmatrices (JADE) [12], and FastICA [12][13][14][15]. ICA has also been employed as a hyperspectral classification approach [16,17].…”

Section: Introductionmentioning

confidence: 99%

Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches

Stites¹,

Gunther

Moon

et al. 2013

Remote Sensing

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This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps.

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New independent component analysis method using higher order statistics with application to remote sensing images

Cited by 31 publications

References 15 publications

A Spatial Contextual Postclassification Method for Preserving Linear Objects in Multispectral Imagery

A Spatial Contextual Postclassification Method for Preserving Linear Objects in Multispectral Imagery

Multi-Temporal Independent Component Analysis and Landsat 8 for Delineating Maximum Extent of the 2013 Colorado Front Range Flood

Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches

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