The sequential maximum angle convex cone (SMACC) endmember model

Gruninger, John; Ratkowski, Anthony J.; Hoke, Michael L.

doi:10.1117/12.543794

Cited by 192 publications

(124 citation statements)

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“…The choice of the ∞ norm results in the selection of the vector with the largest magnitude error in a coefficient associated with one of the singular vectors. Whenever the projection is oblique the active constraint removes a previous projection from the particular model [11]. The processing is illustrated by a simple example.…”

Section: Oblique Projectionsmentioning

confidence: 99%

“…The resulting residual and expansion coefficient arrays can then be further processed with the remaining extremes using the replacement procedure described above. Alternatively, the oblique projection technique [11,12] used for the inequality constraints can be applied directly. The latter option was chosen for this work with the restriction of end-member selection to the list of columns identified as extreme by the linear inequality constraints problem.…”

Section: Non-negative Factorization Of the Matrixmentioning

confidence: 99%

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Band Selection for Hyperspectral Images Using Non-Negativity Constraints

Gruninger

Dothe

2014

REV-JEC

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Abstract-This paper presents a new factorization technique for hyperspectral signal processing based on a constrained singular value decomposition (SVD) approach. Hyperpectral images typically have a large number of contiguous bands that are highly correlated. Likewise the field of view typically contains a limited number of materials and the spectra are also correlated. Only a selected number of bands, the extreme bands that include the dominant materials spectral signatures, are needed to express the data. Factorization can provide a means for interpretation and compression of the spectral data. Hyperspectral images are represented as non-negative matrices by graphic concatenation, with the pixels arranged into columns and each row corresponding to a spectral band. SVD and principal component analysis enjoy a broad range of applications, including, rank estimation, noise reduction, classification and compression, with the resulting singular vectors forming orthogonal basis sets for subspace projection techniques. A key property of non-negative matrices is that their columns/rows form non-negative cones, with any non-negative linear combination of the columns/rows belonging to the cone. Data sets of spectral images and time series reside in non-negative orthants and while subspaces spanned by SVD include all orthants, SVD projections can be constrained to the non-negative orthants. In this paper we utilize constraint sets that confine projections of SVD singular vectors to lie within the cones formed by the spectral data. The extreme vectors of the cone are found and these vectors form a basis for the factorization of the data. The approach is illustrated in an application to hyperspectral data of a mining area collected by an airborne sensor.

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Section: Oblique Projectionsmentioning

confidence: 99%

Section: Non-negative Factorization Of the Matrixmentioning

confidence: 99%

Band Selection for Hyperspectral Images Using Non-Negativity Constraints

Gruninger

Dothe

2014

REV-JEC

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“…The SMACC endmember model was proposed by Gruninger et al [29], which provided a fast and automatic method to find spectral endmembers in the HSI.…”

Section: Smacc Endmember Modelmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation

Zhu

Wen

2018

Remote Sensing

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Abstract:Anomaly detection is an important task in hyperspectral imagery (HSI) processing. It provides a new way to find targets that have significant spectral differences from the majority of the dataset. Recently, the representation-based methods have been proposed for detecting anomaly targets in HSIs. It is essential for this type of method to construct a valid background dictionary to distinguish anomaly and background accurately. In this paper, a novel hyperspectral anomaly detection method based on background estimation and adaptive weighted sparse representation has been proposed. Firstly, to obtain the effective background dictionary without anomaly information, a new background dictionary construction strategy is designed. Secondly, the sparse representation based on the constructed background dictionary is utilized on the dataset. Anomalies and background are distinguished through the response of the residual matrix. Thirdly, the residual matrix is weighted adaptively from global and local domains, which makes anomalies and background more discriminative. An important advantage of the proposed method is that it considers the properties of anomalies in both spectral and spatial domains. Experiments on three HSI datasets reveal that our proposed method achieves an outstanding detection performance compared with the other anomaly detection algorithms.

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“…A forest pixel can usually be deconvolved into 4 basic cover types or end members ( Figure 2): green vegetation (GV), non-photosynthetic vegetation (NPV, i.e., dead wood), soil, and shade [22]. The reflectance of the endmembers for Landsat imagery was extracted using a technique named Sequential Maximum Angle Convex Cone (SMACC) [23], whereas pixel purity index (PPI) was applied for the endmember extraction from MODIS [24]. Although NPV can directly represent the dead wood, the difference of NPV (or NPV + soil) of pre-and post-earthquake in fact had much less correlation with the field measured biomass loss based on preliminary analysis.…”

Section: Satellite Data Analysismentioning

confidence: 99%