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The Hyperspectral infrared imaging technique is an optical detection technology which integrates spectral measurement technology and infrared imaging technology. It can not only obtain the spectral information of the target, but also obtain the image information, which provides a powerful guarantee for the accurate recognition of the target. Due to the spectral characteristics of the mid-wave infrared, mid-infrared hyperspectral imaging system is widely used in civilian and military areas, such as geographical remote sensing, target detection and thermal fluent analysis. With the development of related technologies, imaging spectroscopy technology has diversified. In this paper, the push-type mid-wave infrared reflection planar grating hyperspectral imaging system is selected as the design target. The system consists of: front optical system, slit, collimation system, grating spectroscopic system, imaging optical system, imaging electrical component and control module.

Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.

Oil spill pollution is a severe environmental problem that persists in the marine environment and in inland water systems around the world. Remote sensing is an important part of oil spill response. The hyperspectral images can not only provide the space information but also the spectral information. Pixels of interests generally incorporate information from disparate component that requires quantitative decomposition of these pixels to extract desired information. Oil spill detection can be implemented by applying hyperspectral camera which can collect the hyperspectral data of the oil. By extracting desired spectral signature from hundreds of band information, one can detect and identify oil spill area in vast geographical regions. There are now numerous hyperspectral image processing algorithms developed for target detection. In this paper, we investigate several most widely used target detection algorithm for the identification of surface oil spills in ocean environment. In the experiments, we applied a hyperspectral camera to collect the real life oil spill. The experimental results shows the feasibility of oil spill detection using hyperspectral imaging and the performance of hyperspectral image processing algorithms were also validated.

The infrared imaging systems are normally based on the infrared focal-plane array (IRFPA) which can be considered as an array of independent detectors aligned at the focal plane of the imaging system. Unfortunately, every detector on the IRFPA may have a different response to the same input infrared signal which is known as the nonuniformity problem. Then we can observe the fixed pattern noise (FPN) from the resulting images. Standard nonuniformity correction (NUC) methods need to be recalibrated after a short period of time due the temporal drift of the FPN. Scene-based nonuniformity correction (NUC) techniques eliminate the need for calibration by correction coefficients based on the scene being viewed. However, in the scene-based NUC method the problem of ghosting artifacts widely seriously decreases the image quality, which can degrade the performance of many applications such as target detection and track. This paper proposed an improved scene-based method based on the retina-like neural network approach. The method incorporates the use of non-local means (NLM) method into the estimation of the gain and the offset of each detector. This method can not only estimates the accurate correction coefficient but also restrict the ghosting artifacts efficiently. The proposed method relies on the use of NLM method which is a very successful image denoising method. And then the NLM used here can preserve the image edges efficiently and obtain a reliable spatial estimation. We tested the proposed NUC method by applying it to an IR sequence of frames. The performance of the proposed method was compared the other well-established adaptive NUC techniques.

Infrared imaging system has been applied widely in both military and civilian fields. Since the infrared imager has various types and different parameters, for system manufacturers and customers, there is great demand for evaluating the performance of IR imaging systems with a standard tool or platform. Since the first generation IR imager was developed, the standard method to assess the performance has been the MRTD or related improved methods which are not perfect adaptable for current linear scanning imager or 2D staring imager based on FPA detector. For this problem, this paper describes an evaluation method based on the triangular orientation discrimination metric which is considered as the effective and emerging method to evaluate the synthesis performance of EO system. To realize the evaluation in field test, an experiment instrument is developed. And considering the importance of operational environment, the field test is carried in practical atmospheric environment. The test imagers include panoramic imaging system and staring imaging systems with different optics and detectors parameters (both cooled and uncooled). After showing the instrument and experiment setup, the experiment results are shown. The target range performance is analyzed and discussed. In data analysis part, the article gives the range prediction values obtained from TOD method, MRTD method and practical experiment, and shows the analysis and results discussion. The experimental results prove the effectiveness of this evaluation tool, and it can be taken as a platform to give the uniform performance prediction reference.

Using super-resolution (SR) technology is a good approach to obtain high-resolution infrared image. However, Image super-resolution reconstruction is essentially an ill-posed problem, it is important to design an effective regularization term (image prior). Gaussian prior is widely used in the regularization term, but the reconstructed SR image becomes over-smoothness. Here, a novel regularization term called non-local means (NLM) term is derived based on the assumption that the natural image content is likely to repeat itself within some neighborhood. In the proposed framework, the estimated high image is obtained by minimizing a cost function. The iteration method is applied to solve the optimum problem. With the progress of iteration, the regularization term is adaptively updated. The proposed algorithm has been tested in several experiments. The experimental results show that the proposed approach is robust and can reconstruct higher quality images both in quantitative term and perceptual effect.

Image registration is crucial in various image fusion tasks, like super-resolution. For the success of super-resolution reconstruction, it is essential to find out high accurate subpixel motion estimation between the input images sequence. This paper proposes a frequency domain-based motion estimation algorithm for the under-sampled infrared images. The proposed algorithm which only considers pure translational motion is based on the phase-only correlation. Due to the discrete Fourier transform and subpixel displacement, the signal peak is not always concentrate in the integer coordinates. Thus, the signals adjacent to peak are utilized to estimate aliasing influence. Excellent results are obtained for subpixel translation estimation. The algorithm is also compared to other algorithms, and the analyses show that this algorithm is more robust and accurate.

The micrograph and the geographical information system(GIS) technology are combined, and applied into histiocytic anatomy. Through studying histiocytic changes of Cleistogenes squarrosa's vegetation organs, namely leaf and stem, the steppe plants' inherent mechanism of miniaturization is revealed. In the course of restoring succession, Cleistogenes squarrosa's anatomy of leaf and stem demonstrate the same variation trend in the three different sample plots: the longer the resume time is, the more, its cells which make up the organ are. According to opposite course, miniaturization has all taken place in the leaf and stem. However, there is difference in the miniaturization mechanism of the leaf and stem. (1) According to dissection structure of the blade, the reduction of organizing the figure of the mesophyll has caused miniaturization. (2) The miniaturization mechanism of the stem is the reduction of different organization's cell's figure of the stem.