Dual-Gratings Imaging Spectrometer

Common dispersive-type spectroscopic instruments include prism-type and grating-type, usually using a single dispersive element. The continuous imaging band is always limited by the dispersion angle. When it is necessary to image two wavebands with an ultra-spectral resolution that are far apart, the imaging is difficult due to the large diffraction angle. To broaden the spectral coverage of the imaging spectrometer, in this paper, we propose a dual-gratings imaging spectrometer with two independently rotating gratings. In this proposed system, two very far apart wavelength bands can be imaged in the adjacent areas by adjusting the angle of the dual gratings. This greatly expands the spectral coverage of the imaging spectrometer. Currently, the only application area considered for this instrument is solar applications. In this article, we present the optical system of the dual-gratings imaging spectrometer, illustrate several advantages of the new structure, and discuss new problems caused by the dual-gratings, which are referred to as overlap between two spectra and double image offset. We deduced the calculation process of the dual grating rotation angle, the relationship between the final acquired image and the slit, the relationship between the angle change between the dual gratings and the double image offset, and the relationship between the MTF upper limit reduction and the spatial frequency. This article also summarizes the shortcomings of this structure and studies the applicable fields under these shortcomings. At last, we simulate a dual-gratings imaging spectrometer system, compare this scheme with two traditional schemes, and conclude that this instrument has certain practical significance

A Novel Method of Aircraft Detection Based on High-Resolution Panchromatic Optical Remote Sensing Images

In target detection of optical remote sensing images, two main obstacles for aircraft target detection are how to extract the candidates in complex gray-scale-multi background and how to confirm the targets in case the target shapes are deformed, irregular or asymmetric, such as that caused by natural conditions (low signal-to-noise ratio, illumination condition or swaying photographing) and occlusion by surrounding objects (boarding bridge, equipment). To solve these issues, an improved active contours algorithm, namely region-scalable fitting energy based threshold (TRSF), and a corner-convex hull based segmentation algorithm (CCHS) are proposed in this paper. Firstly, the maximal variance between-cluster algorithm (Otsu’s algorithm) and region-scalable fitting energy (RSF) algorithm are combined to solve the difficulty of targets extraction in complex and gray-scale-multi backgrounds. Secondly, based on inherent shapes and prominent corners, aircrafts are divided into five fragments by utilizing convex hulls and Harris corner points. Furthermore, a series of new structure features, which describe the proportion of targets part in the fragment to the whole fragment and the proportion of fragment to the whole hull, are identified to judge whether the targets are true or not. Experimental results show that TRSF algorithm could improve extraction accuracy in complex background, and that it is faster than some traditional active contours algorithms. The CCHS is effective to suppress the detection difficulties caused by the irregular shape

Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform

This study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images. First, the source images are decomposed by CAST method into base-layer and detail-layer sub-images. Secondly, for the base-layer components with larger-scale intensity variation, the LatLRR, is a valid method to extract the salient information from image sources, and can be applied to generate saliency map to implement the weighted fusion of base-layer images adaptively. Meanwhile, the regularization term of zero crossings in differences, which is a classic method of optimization, is designed as the regularization term to construct the fusion of detail-layer images. By this method, the gradient information concealed in the source images can be extracted as much as possible, then the fusion image owns more abundant edge information. Compared with other state-of-the-art algorithms on publicly available datasets, the quantitative and qualitative analysis of experimental results demonstrate that the proposed method outperformed in enhancing the contrast and achieving close fusion result

Infrared and Visible Image Fusion Based on Co-Occurrence Analysis Shearlet Transform

This study based on co-occurrence analysis shearlet transform (CAST) effectively combines the latent low rank representation (LatLRR) and the regularization of zero-crossing counting in differences to fuse the heterogeneous images. First, the source images are decomposed by CAST method into base-layer and detail-layer sub-images. Secondly, for the base-layer components with larger-scale intensity variation, the LatLRR, is a valid method to extract the salient information from image sources, and can be applied to generate saliency map to implement the weighted fusion of base-layer images adaptively. Meanwhile, the regularization term of zero crossings in differences, which is a classic method of optimization, is designed as the regularization term to construct the fusion of detail-layer images. By this method, the gradient information concealed in the source images can be extracted as much as possible, then the fusion image owns more abundant edge information. Compared with other state-of-the-art algorithms on publicly available datasets, the quantitative and qualitative analysis of experimental results demonstrate that the proposed method outperformed in enhancing the contrast and achieving close fusion result