Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images

One of the challenges in hyperspectral data analysis is the presence of mixed pixels. Mixed pixels are the result of low spatial resolution of hyperspectral sensors. Spectral unmixing methods decompose a mixed pixel into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fraction values, NMF based methods are well suited to this problem. In this paper multilayer NMF has been used to improve the results of NMF methods for spectral unmixing of hyperspectral data under the linear mixing framework. Sparseness constraint on both spectral signatures and abundance fractions matrices are used in this paper. Evaluation of the proposed algorithm is done using synthetic and real datasets in terms of spectral angle and abundance angle distances. Results show that the proposed algorithm outperforms other previously proposed methods.Comment: 4 pages, conferenc

Unmixing of Hyperspectral Data Using Robust Statistics-based NMF

Mixed pixels are presented in hyperspectral images due to low spatial resolution of hyperspectral sensors. Spectral unmixing decomposes mixed pixels spectra into endmembers spectra and abundance fractions. In this paper using of robust statistics-based nonnegative matrix factorization (RNMF) for spectral unmixing of hyperspectral data is investigated. RNMF uses a robust cost function and iterative updating procedure, so is not sensitive to outliers. This method has been applied to simulated data using USGS spectral library, AVIRIS and ROSIS datasets. Unmixing results are compared to traditional NMF method based on SAD and AAD measures. Results demonstrate that this method can be used efficiently for hyperspectral unmixing purposes.Comment: 4 pages, conferenc

Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data

Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem. In this paper we have used graph regularized NMF (GNMF) method combined with sparseness constraint to decompose mixed pixels in hyperspectral imagery. This method preserves the geometrical structure of data while representing it in low dimensional space. Adaptive regularization parameter based on temperature schedule in simulated annealing method also has been used in this paper for the sparseness term. Proposed algorithm is applied on synthetic and real datasets. Synthetic data is generated based on endmembers from USGS spectral library. AVIRIS Cuprite dataset is used as real dataset for evaluation of proposed method. Results are quantified based on spectral angle distance (SAD) and abundance angle distance (AAD) measures. Results in comparison with other methods show that the proposed method can unmix data more effectively. Specifically for the Cuprite dataset, performance of the proposed method is approximately 10% better than the VCA and Sparse NMF in terms of root mean square of SAD.Comment: 10 pages, Journa

Hyperspectral Data Unmixing Using GNMF Method and Sparseness Constraint

Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem. In this paper we have used graph regularized (GNMF) method with sparseness constraint to unmix hyperspectral data. This method applied on simulated data using AVIRIS Indian Pines dataset and USGS library and results are quantified based on AAD and SAD measures. Results in comparison with other methods show that the proposed method can unmix data more effectively.Comment: 4 pages, conferenc

On-Line Object Feature Extraction for Multispectral Scene Representation

This thesis investigates a new on-line unsupervised object-feature extraction method that reduces the complexity and costs associated with the analysis of the multispectral image data and the data transmission, storage, archival and distribution as well. Typically in remote sensing a scene is represented by the spatially disjoint pixel-oriented features. It would appear possible to reduce data redundancy by an on-line unsupervised object-feature extraction process, where combined spatial-spectral object\u27s features, rather than the original pixel-features, are used for multispectral scene representation. The ambiguity in the object detection process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the decision making process. We define the unity relation that must exist among the pixels of an object. The unity relation can be constructed with regard to the: adjacency relation, spectral-feature and spatial-feature characteristics in an object; e.g. AMICA (Automatic Multispectral Image Compaction Algorithm) uses the within object pixel feature gradient vector as a valuable contextual information to construct the object\u27s features, which preserve the class separability information within the data. For on-line object extraction, we introduce the path-hypothesis, and the basic mathematical tools for its realization are introduced in terms of a specific similarity measure and adjacency relation. AMICA is an example of on-line preprocessing algorithm that uses unsupervised object feature extraction to represent the information in the multispectral image data more efficiently. As the data are read into the system sequentially, the algorithm partitions the observation space into an exhaustive set of disjoint objects simultaneously with the data acquisition process, where, pixels belonging to an object form a path-segment in the spectral space. Each path-segment is characterized by an object-feature set. Then, the set of object-features, rather than the original pixel-features, is used for data analysis and data classification. AMICA is applied to several sets of real image data, and the performance and reliability of features is evaluated. Example results show an average compaction coefficient of more than 20/1 (this factor is data dependent). The classification performance is improved slightly by using object-features rather than the original data, and the CPU time required for classification is reduced by a factor of more than 20 as well. The feature extraction process may be implemented in real time, thus the object-feature extraction CPU time is neglectable; however, in the simulated satellite environment the CPU time for this process is less than 15% of CPU time for original data classification

Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has not the limitation of linear discriminant analysis (LDA) in the number of extracted features. In DA-PC1, the dominant structure of distribution is preserved by PC1 and the class separability is increased by DA. The experimental results show the good performance of DA-PC1 compared to some state-of-the-art feature extraction methods

Deformable Contour-Based Maneuvering Flying Vehicle Tracking in Color Video Sequences

This paper presents a new method for the tracking of maneuvering flying vehicles using a deformable contour model in color video sequences. The proposed approach concentrates on targets with maneuvering motion in sky, which involves fundamental aspect change stemmed from 3D rotation of the target or video camera. In order to segment and track the aircraft in a video, at first, the target contour is initialized manually in a key frame, and then it is matched and tracked automatically in the subsequent frames. Generally active contour models employ a set of energy functions based on edge, texture, color, and shape features. Afterwards, objective function is minimized iteratively to track the target contour. In the proposed algorithm, we employ game of life cellular automaton to manage snake pixels’ (snaxels’) deformation in each epoch of minimization procedure. Furthermore, to cope with the large aspect change of aircraft, a Gaussian model has been taken into account to represent the target color in RGB space. To compensate for changes in luminance and chrominance ingredients of the target, the prior distribution function is dynamically updated during tracking. The proposed algorithm is evaluated using the collected dataset, and the expected probability of tracking error is calculated. Experimental results show positive results for the proposed algorithm.</jats:p