Danfeng Liu

Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery

Mixed pixels are widely existent in remote-sensing imagery. Although the proportion occupied by each class in mixed pixels can be determined by spectral unmixing, the spatial distribution of classes remains unknown. Sub-pixel mapping (SPM) addresses this problem and a sub-pixel/pixel spatial attraction model (SPSAM) has been introduced to realize SPM. However, this algorithm fails to adequately consider the correlation between sub-pixels. Consequently, the SPM results created by SPSAM are noisy and the accuracy is limited. In this article, a method based on particle swarm optimization is proposed as post-processing on the SPM results obtained with SPSAM. It searches the most likely spatial distribution of classes in each coarse pixel to improve the SPSAM. Experimental results show that the proposed method can provide higher accuracy and reduce the noise in the results created by SPSAM. When compared with the available modified pixel-swapping algorithm, the proposed method often yields higher accuracy results.

Geometric Method of Fully Constrained Least Squares Linear Spectral Mixture Analysis

Spectral unmixing is one of the important techniques for hyperspectral data processing. The analysis of spectral mixing is often based on a linear, fully constrained (FC) (i.e., nonnegative and sum-to-one mixture proportions), and least squares criterion. However, the traditional iterative processing of FC least squares (FCLS) linear spectral mixture analysis (LSMA) (FCLS-LSMA) is of heavy computational burden. Recently developed geometric LSMA methods decreased the complexity to some degree, but how to further reduce the computational burden and completely meet the FCLS criterion of minimizing the unmixing residual needs to be explored. In this paper, a simple distance measure is proposed, and then, a new geometric FCLS-LSMA method is constructed based on the distance measure. The method is in line with the FCLS criterion, free of iteration and dimension reduction, and with very low complexity. Experimental results show that the proposed method can obtain the same optimal FCLS solution as the traditional iteration-based FCLS-LSMA, and it is much faster than the existing spectral unmixing methods, particularly the traditional iteration-based method.

Fast Implementation of Maximum Simplex Volume-Based Endmember Extraction in Original Hyperspectral Data Space

Endmember extraction (EE) is a prerequisite task for spectral analysis of hyperspectral imagery. In all kinds of EE algorithms, maximum simplex volume-based ones, such as simplex growing algorithm (SGA) and N-FINDR algorithm, have been widely used for their fully automated and efficient performance. However, implementation of the algorithms needs dimension reduction of original data, and the algorithms include innumerable volume calculation. This leads to a low speed of the algorithms and thus becomes a limitation to their applications. In this paper, a simple distance measure is presented, and then, fast SGA and fast N-FINDR algorithm are constructed based on a proposed distance measure, which is free of dimension reduction and makes use of distance measure instead of volume evaluation to speed up the algorithm. The complexity of the proposed methods is compared with the original algorithms by theoretical analysis. Experiments show that the implementation of the two improved EE algorithms is much faster than that of the two original maximum simplex volume-based EE algorithms.

Integration of spatial attractions between and within pixels for sub-pixel mapping

As a promising technique to enhance the spatial resolution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and between them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub-pixel/pixel spatial attraction model (SPSAM), are described by the modified SPSAM (MSPSAM) that estimates the attractions according to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model (MSAM) for sub-pixel mapping is proposed that integrates the spatial attractions both within pixels and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is employed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM (MPS), MSAM can provide higher accuracy and more rational sub-pixel mapping results.

Spectral Unmixing Model Based on Least Squares Support Vector Machine With Unmixing Residue Constraints

Spectral unmixing has been an important technique for hyperspectral imagery processing. In traditional spectral unmixing methods that are based on the linear spectral mixture model (LSMM), unmixing accuracy is limited by the inherent deficiency of the model. It was shown that the support vector machine (SVM) can be extended for spectral unmixing, based on the advantage that the SVM model can accommodate the variations within a relative pure class by using multiple pure samples instead of a single endmember for one class. In the SVM model, class label errors are considered in constraints. However, the errors concerned in spectral unmixing are the unmixing residue instead of the class label ones. This letter presents a method of imposing unmixing residue constraints on the least squares SVM unmixing model. The related problems, including deducing the closed-form solution and substituting the single endmember for multiple ones, were studied together. Experiments showed that the new SVM model was superior to the original SVM as well as the traditional LSMM in terms of unmixing residue, fractional abundance, and confused matrix criterions.

Sub-pixel mapping based on sub-pixel to sub-pixel spatial attraction model

In this paper, a new sub-pixel mapping algorithm is proposed based on sub-pixel/sub-pixel spatial attraction model (SSSAM). Different from the original sub-pixel/pixel spatial attraction model (SPSAM), the SSSAM considers the spatial distribution of each sub-pixel within neighbor pixels, when calculating the spatial attractions for sub-pixels within the centre pixel. Then the attractions are used to determine the class values of these sub-pixels. Two experiments on three artificial images and one real remote sensing image are processed. Both of the results show that compared with traditional SPSAM, the proposed method can produce sub-pixel mapping results with higher accuracy.

Endmember extraction based on modified Iterative Error Analysis

Iterative Error Analysis (IEA) widely known as a good endmember extraction (EE) algorithm. It is robust, automatic and free of data transformation. However, IEA is faced with risks in some cases due to the sole use of unmxing distance, and its speed is lowed down by the iteration-based linear spectral mixture analysis (LSMA). To make IEA algorithm faster and more robust, its modified version is proposed based on two substitutions. One is substituting integrated distance for unmxing distance, which makes the algorithm more robust. The other is substituting SVM-based multiple endmember spectral mixture analysis (MESMA) for iteration-based LSMA, which speeds up the algorithm greatly. Experiments show that the modified IEA algorithm outperforms original one in terms of both robustness and running speed.

Interactive multi-image colour visualization for hyperspectral imagery

ABSTRACT Data visualization can accelerate data processing so that enormous quantities of data can be utilized effectively. Visualization of data can achieve image communication between people and data as well as between people to help observers get information hidden in data, providing a tool for discovery and understanding of scientific law. To solve the problem of multi-image and multi-modality image display in the field of remote sensing, an interactive colour visualization method for hyperspectral imagery (HSI) is proposed in this article. This method visualizes complex information of original HSI data through different fusion results of multiple images in a colour space, which is under the interactive control of the observers. By gradually determining predetermined points, observers can obtain a relatively satisfying image blending mode, output an image with clearer interested target, and obtain the corresponding mixing coefficient of images. The proposed method can also solve the problem that traditional visualization methods only display information from three bands in one image, and conduct information mining in HSI with a certain purpose based on the demands of users. In addition, this approach is also applicable for visualization of other types of multi-modal imagery.

An interactive color visualization method with multi-image fusion for hyperspectral imagery

An interactive color visualization method is proposed for hyperspectral imagery (HSI). The method visualizes complex information through different fusion results of multiple images in a color space which is under the interactive control of the observers. In order to solve the main problem of traditional visualization methods, i.e., they can at most display information from three bands in one image, this paper proposes an easy, vivid and effective method for color visualization. In the proposed approach, observers interactively control a cursor position to change the output fusion images and their fusion coefficients. In the approach, the dynamic display will include more than three bands of HSIs. The proposed method is also applicable for visualization of other multi-images, e.g., multispectral images, output images of direction filters, multi-focus images, and multi-temporal images, etc.