The spectral signatures in most hyperspectral classification approaches are generally treated as random vectors, which is inappropriate in denoting their typical physical characteristics, such as central wavelengths, widths, and depths of absorption bands. In this paper, we present a new classification approach by enhancing the absorption bands of spectral signatures to boost their physical information. Firstly, an analysis is made of the characteristics of absorption bands of spectral signatures. Next, an absorption bands enhancing approach is proposed based on the discussion of the approach of fusing spectral signatures and their derivative. Finally, the proposed approach is applied on two real hyperspectral subimages. The experimental results show that our proposed approach can significantly enhance the differences of spectral signatures of a hyperspectral images. And thus can improve the classification performance of hyperspectral images.
Due to the limited depth-of-focus of optical lenses in imaging camera, it is impossible to acquire an image with all parts of the scene in focus. To make up for this defect, fusing the images at different focus settings into one image is a potential approach and many fusion methods have been developed. However, the existing methods can hardly deal with the problem of image detail blur. In this paper, a novel multiscale geometrical analysis called the directional spectral graph wavelet transform (DSGWT) is proposed, which integrates the nonsubsampled directional filter bank with the traditional spectral graph wavelet transform. Through combines the feature of efficiently representing the image containing regular or irregular areas of the spectral graph wavelet transform with the ability of capturing the directional information of the directional filter bank, the DSGWT can better represent the structure of images. Given the feature of the DSGWT, it is introduced to multi-focus image fusion to overcome the above disadvantage. On the one hand, using the high frequency subbands of the source images are obtained by the DSGWT, the proposed method efficiently represents the source images. On the other hand, using morphological filter to process the sparse feature matrix obtained by sum-modified-Laplacian focus measure criterion, the proposed method generates the fused subbands by morphological filtering. Comparison experiments have been performed on different image sets, and the experimental results demonstrate that the proposed method does significantly improve the fusion performance compared to the existing fusion methods.
Complex background suppression is a key problem in the detection of the infrared dim small target at far distance. In this paper, a background suppression method for the dim small target detection based on the combination of the high-order diffusion equation and the RX operator is proposed. Firstly, the high-order diffusion equation is applied to decompose the original infrared image, and the multiscale features of the image are extracted. Then, by the fact that the signal coefficients of target are different with that of background clutter in the decomposed sub-image, the RX operator is utilized to separate the dim small target and the background clutter. Two groups of experimental results demonstrate that the complex background can be suppressed by the presented method effectively, whose performance is better than that of the max median (MMed) method. The proposed method can preserve and enhance the infrared target signal effectively whose SCR is greater than 1.7.
Fusion of infrared and visible images is a significant research area in image analysis and computer vision. The purpose of infrared and visible image fusion is to combine the complementary image information of the source images into a fused image. Thus, it is vital to efficiently represent the important image information of the source images and choose rational fusion rules. To achieve this aim, an image fusion method using multiscale directional nonlocal means (MDNLM) filter is proposed in this paper. The MDNLM combines the feature of preserving edge information by the nonlocal means filter with the capacity of capturing directional image information by the directional filter bank, which can effectively represent the intrinsic geometric structure of images. The MDNLM is a multiscale, multidirectional, and shift-invariant image decomposition method, and we use it to fuse infrared and visible images in this paper. First, the MDNLM is discussed and used to decompose the source images into approximation subbands and directional detail subbands. Then, the approximation and directional detail subbands are fused by a local neighborhood gradient weighted fusion rule and a local eighth-order correlation fusion rule, respectively. Finally, the fused image can be obtained through the inverse MDNLM. Comparison experiments have been performed on different image sets, and the results clearly demonstrate that the proposed method is superior to some conventional and recent proposed fusion methods in terms of the visual effects and objective evaluation.
In an integral imaging (II) system, the pickup sampling effects play an important role in affecting the blur of an integral image. In this paper, the blur property of an integral image due to the pickup sampling artifacts is first analyzed. Then, a figure of merit-the edge blur width (EBW) of a white and black bar object is proposed to characterize the blur of the reconstructed image, and its theoretical model is derived in detail based on a continuous/discrete (C/D) sampling mechanism by considering both the pickup sampling and the reconstruction process. Further, the quantitative relationships of the blur with the pickup sampling parameters (the pixel number of each elemental image, the number of elemental images) are calculated by the EBW model and measured by C/D sampling II simulation experiments, respectively. We find out that the theoretical results have a good agreement with the estimated ones, and the minimum values of the EBW occurred periodically when the pixel number of an elemental image is an integral multiple of the magnification ratio.
According to the characteristic of single-element detector and non-imaging spectroradiometer, a new imaging FTIR spectroradiometer system was developed for spectral data acquisition This system is composed of a spectroradiometer, a synchronous controller and a scanning device. Using the data interface of spectroradiometer, spectral radiometric calibration can be achieved for the system. The image resolution is 500 x 500 pixels, spectral range is 667-5000 cm(-1), spectral resolution is 1 cm(-1), and space Field of view is 150 degrees, Instant Field of View is 0.3 degrees. Experiments were held for actual data acquisition and data analysis was made. The analysis result indicates that the proposed system is adequate for non-realtime imaging spectral data acquisition
Infrared and visible image fusion technique is a popular topic in image analysis because it can integrate complementary information and obtain reliable and accurate description of scenes. Multiscale transform theory as a signal representation method is widely used in image fusion. In this paper, a novel infrared and visible image fusion method is proposed based on spectral graph wavelet transform (SGWT) and bilateral filter. The main novelty of this study is that SGWT is used for image fusion. On the one hand, source images are decomposed by SGWT in its transform domain. The proposed approach not only effectively preserves the details of different source images, but also excellently represents the irregular areas of the source images. On the other hand, a novel weighted average method based on bilateral filter is proposed to fuse low- and high-frequency subbands by taking advantage of spatial consistency of natural images. Experimental results demonstrate that the proposed method outperforms seven recently proposed image fusion methods in terms of both visual effect and objective evaluation metrics.
For the sake of effectively alleviating the effect of noise in infrared spectral data, a method of infrared spectral data denoising based on stationary wavelet transform is proposed in this paper. Firstly, stationary wavelet transform is adopted to decompose the original infrared spectral data, which extracts data of multi-scale specific characteristic. Secondly, according to difference between spectral signal and noise in different scales, the improved variational method is introduced to adjust each sub-band coefficients. Finally, denoised signal was reconstructed through inverse stationary wavelet transform. Several groups of experimental results are demonstrated that the proposed method not only effectively extract noise but also decreases Mean Squared Error and preserve character of signal. It can be utilized in the actual infrared spectral data denosing and achieved perfect effectiveness.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(144 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(216 450 300)'/%3e%3cuse xlink:href='%23a' transform='rotate(288 450 300)'/%3e%3c/svg%3e)