To meet the demanding of spectral reconstruction in the visible and near-infrared wavelength, the spectral reconstruction method for typical surface types is discussed based on the USGS /ASTER spectral library and principal component analysis (PCA). A new spectral reconstructed model is proposed by the information of several typical bands instead of all of the wavelength bands, and a linear combination spectral reconstruction model is also discussed. By selecting 4 typical spectral datasets including green vegetation, bare soil, rangeland and concrete in the spectral range of 400-900 nm, the PCA results show that 6 principal components could characterized the spectral dataset, and the relative reconstructed errors are smaller than 2%. If only 6-7 selected typical bands are employed to spectral reconstruction for all the surface reflectance in 400-900 nm, except that the reconstructed error of green vegetation is about 3.3%, the relative errors of other 3 datasets are all smaller than 1.6%. The correlation coefficients of those 4 datasets are all larger than 0.99, which can effectively satisfy the needs of spectral reconstruction. In addition, based on the spectral library and the linear combination model of 4 common used bands of satellite remote sensing such as 490, 555, 670 and 865 nm, the reconstructed errors are smaller than 8.5% in high reflectance region and smaller than 1.5% in low reflectance region respectively, which basically meet the needs of spectral reconstruction. This study can provide a reference value for the surface reflectance processing and spectral reconstruction in satellite remote sensing research.

Study on the spectral reconstruction of typical surface types based on spectral library and principal component analysis

Target detection of hyperspectral image has always been a hot research topic, especially due to its important applications in military and civilian remote sensing. This paper employs the idea of classification and proposes a novel detection framework which incorporates dictionary learning and discriminative information. Due to the fact that target pixels lie in different subspace with background pixels, a novel detection model is proposed. In addition, a linear kernel is applied to project the image data into high-dimensional space, separating the target pixels and background pixels. Synthetic image and popular real hyperspectral image are used to evaluate our algorithm. Experimental results indicate that our proposed detector outperforms the traditional detection methods.

Dictionary learning based target detection for hyperspectral image

The infrared dynamic scene projector is a key component of the infrared imaging seeker semi-physical simulation system. Aiming at the long-wave infrared imaging seeker system, the design idea of long-wave infrared dynamic scene projector with IR-CRT as the core device was proposed. The working principle of IR-CRT was introduced. The optical collimation system was designed. The key parameters such as resolution, distortion, simulated temperature range and uniformity were tested. The dynamic scene projector had been successfully applied to infrared imaging seeker. The semi-physical simulation system had verified the static and dynamic tracking performance of the infrared imaging seeker, which met the requirements of product use, and promoted the product development process.

Design and test of long-wave infrared dynamic scene projector based on IR-CRT

Camera’s internal parameter calibration is an important problem in computer vision. The moment, a precise targets like checkboard is required when camera calibration is performed. But this method is not suitable for a telephoto lens. In order to overcome this problem, a new camera calibration model is constructed by installing the debugged telephoto camera on an accurate two-dimensional rotating platform. Let the camera move around the rotating axis of the rotating platform. The camera takes pictures of distant objects directly. Then, the next picture is get by rotating the two-dimensional platform. The image coordinates , of the same point in space, in different images are obtained by image matching. The motion matrix of the camera is calculated from the readings of the two-dimensional rotating platform. Last, the optimization equation can be enumerated to solve the internal parameters. The experimental results show that the internal parameters obtained by the algorithm can satisfy the calculation accuracy.

Camera internal parameter calibration based on rotating platform and image matching

Deep-sea optical imaging is an important method to explore mineral resource and detect biological or chemical activities in deep sea. As the premise of deep-sea imaging system, the deep-sea optical lighting system has a huge impact on the quality of optical images. And it has profound scientific significance and application prospect to improve the efficiency of deep-sea diving and save the cost of deep-sea expedition. In order to solve the problems of colour distortion and uneven brightness in deep-sea optical images, the investigation of non-uniform illumination design based on light attenuation is carried out in this paper. Firstly, the attenuation characteristics of light in deep-sea are established. Then the deep-sea lens and lens group are designed based on the light attenuation characteristic. The design shows that the light distribution area of the deep-sea lamp is rectangular, and the illumination uniformity of the design is more than 75% in sea-water. Finally, we can conclude that the attenuation characteristics of light can be used to design a better deep-sea lamp.

The deep-sea lamp design based on the light attenuation in water

In infrared images, when the traditional histogram equalizes the image, the detail pixels are easily immerged by the background pixels, resulting in the image being too bright and too dark. Based on this situation, an adaptive inverse histogram equalization algorithm was proposed in this paper. The algorithm enhanced image details by inverse statistics, adaptive selection threshold and segmentation mapping. Compared with the traditional histogram equalization algorithm, the inverse histogram equalization algorithm significantly improve the image visual effect in different gray level distributions and enhance the details of different areas of the image to different degrees. Moreover, under the premise of achieving better image processing effects, this algorithm can still guarantee real-time performance and high efficiency by optimizing calculation methods, and is suitable for FPGA hardware transplantation.

Cao Haijie

Papers

Infrared image adaptive inverse histogram enhancement technology