Proc. SPIE. 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
KEYWORDS: Hyperspectral imaging, Image visualization, Principal component analysis, Visual process modeling, Data modeling, Visualization, Optimization (mathematics), Dimension reduction, Stochastic processes, RGB color model
Hyperspectral image visualization reduces high-dimensional spectral bands to three color channels, which are sought in order to explain well the nonlinear data characteristics that are hidden in the high-dimensional spectral bands. Despite the surge in the linear visualization techniques, the development of nonlinear visualization has been limited. The paper presents a new technique for visualization of hyperspectral image using t-distributed stochastic neighbor embedding, called VHI-tSNE, which learns a nonlinear mapping between the high-dimensional spectral space and the three-dimensional color space. VHI-tSNE transforms hyperspectral data into bilateral probability similarities, and employs a heavy-tailed distribution in three-dimensional color space to alleviate the crowding problem and optimization problem in SNE technique. We evaluate the performance of VHI-tSNE in experiments on several hyperspectral imageries, in which we compare it to the performance of other state-of-art techniques. The results of experiments demonstrated the strength of the proposed technique.
A novel intelligent model for Image Processing (IP) research integrated development environment (IDE) is presented
for rapid converting conceptual model of IP algorithm into computational model and program implementation. Considering psychology of IP and computer programming, this model presents a cycle model of IP research process and establishes an improved expert system prototype. Visualization approaches are introduced into visualizing three phases of IP development. An intelligent methodology is applied to reuse algorithms, graphical user interfaces (GUI) and data visualizing tools. Thus, researchers are allowed to fix more attention only on their own interest algorithm models. Experimental results show that the development based the new model enhances rapid algorithm prototype modeling with great efficiency and speed.
To detect and track moving dim targets against the complex cluttered background in IR image sequences is still a difficult problem because the nonstationary structured background clutter usually results in low target detectability and a high probability of false alarm. A new adaptive anisotropic filter based on a modified partial differential equation (AFMPDE) is proposed to detect a small target in such a strong cluttered background. A regularizing operator is employed to adaptively eliminate structured background and simultaneously enhance the target signal. The proposed algorithm's performance is illustrated and compared with the two-dimensional least mean square (TDLMS) adaptive filter on real IR image data. Experimental results demonstrate that the proposed novel method is fast and effective.
We propose a fast method for linearizing the edge-preserving regularization. The regularization operator is decomposed to the sum of some linear operators. The phase-only image is used in place of the estimated image. Those linear operators are computed from the phase-only image. They are the approximation of the true regularization operator. The new discontinuity maps do not need to be computed from the last image estimate at every step of the algorithm. This fast method can reduce much algorithm processing time.
To reduce ringing and preserve edges, smoothness must be adaptive in image restoration. We improve on the Laplacian operator and propose an anisotropic regularizing operator. The variables are substitutes for the constants in the Laplacian operator. The anisotropic regularizing operator can control adaptively the direction and amount of smoothing in image restoration. Although the anisotropism idea is closely related to edge-preserving regularization, the anisotropic regularizing operator has different regularization terms and simpler conditions. The iterative equations of anisotropic regularizing operators have unified form. By imposing some constraints, iterative equations can become equations of image restoration with ringing reduction and edge-preserving regularization. The method for linearizing the anisotropic regularization term is provided.