Optical SAR data processing configuration with simultaneous azimuth and range matching filtering.

The real-time processing of synthetic aperture radar (SAR) data has a high requirement for the processor, which is a difficult problem in SAR real-time processing. With the rapid development of optoelectronic devices, traditional electrical SAR data processing can be converted into optoelectronic processing to improve the processing speed. In this paper, a new type of optical device is proposed to improve the processing speed of SAR data. With the help of a spatial light modulator (SLM), the initial SAR signal and matched filter function are loaded on the input plane and spectrum plane of the 4f system, respectively. Using an optical lens with the function of the Fourier transform, the Fourier transform and inverse Fourier transform of the SAR signal are carried out to realize the fast imaging of SAR. In theory, the processing speed of SAR data is the speed of light. Compared with traditional methods such as the range-Doppler (RD) algorithm, it is no longer necessary to carry out a one-dimensional Fourier transform but to carry out matching filtering for the azimuth and range of the spectrum plane of 4f system at the same time. In this way, it is not necessary to introduce a cylindrical lens, only a spherical lens is needed to realize the Fourier transform imaging of SAR. Finally, a two-dimensional SAR processing optical system is built to obtain the SAR image in real time.

A High Optical Throughput Spectral Imaging Technique Using Broadband Filters.

To address the miniaturization of the spectral imaging system required by a mounted platform and to overcome the low luminous flux caused by current spectroscopic technology, we propose a method for the multichannel measurement of spectra using a broadband filter in this work. The broadband filter is placed in front of a lens, and the spectral absorption characteristics of the broadband filter are used to achieve the modulation of the incident spectrum of the detection target and to establish a mathematical model for the detection of the target. The spectral and spatial information of the target can be obtained by acquiring data using a push-broom method and reconstructing the spectrum using the GCV-based Tikhonov regularization algorithm. In this work, we compare the accuracy of the reconstructed spectra using the least-squares method and the Tikhonov algorithm based on the L-curve. The effect of errors in the spectral modulation function on the accuracy of the reconstructed spectra is analyzed. We also analyze the effect of the number of overdetermined equations on the accuracy of the reconstructed spectra and consider the effect of detector noise on the spectral recovery. A comparison between the known data cubes and our simulation results shows that the spectral image quality based on broadband filter reduction is better, which validates the feasibility of the method. The proposed method of combining broadband filter-based spectroscopy with a panchromatic imaging process for measurement modulation rather than spectroscopic modulation provides a new approach to spectral imaging.

Long-Distance Sub-Diffraction High-Resolution Imaging Using Sparse Sampling.

How to perform imaging beyond the diffraction limit has always been an essential subject for the research of optical systems. One effective way to achieve this purpose is Fourier ptychography, which has been widely used in microscopic imaging. However, microscopic imaging measurement technology cannot be directly extended to imaging macro objects at long distances. In this paper, a reconstruction algorithm is proposed to solve the need for oversampling low-resolution images, and it is successfully applied to macroscopic imaging. Compared with the traditional FP technology, the proposed sub-sampling method can significantly reduce the number of iterations in reconstruction. Experiments prove that the proposed method can reconstruct low-resolution images captured by the camera and achieve high-resolution imaging of long-range macroscopic objects.

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.

[A compression algorithm for multi-spectral TDICCD image].

In order to solve the problem of edge and texture blurring of multi-spectral image caused by the wavelet transform and bit-plane encoder of traditional method not taking into account image characteristics, a compression algorithm of multi-spectral TDICCD image having few bands is proposed in the present paper. The proposed direction-adaptive lifting DWT can adaptively choose the best lifting direction and use the Lagrange interpolation technique to make prediction according to its local characteristics. This method makes good use of the image texture features. The proposed rate control algorithm can adaptively allocate the rate bits according to image content. The experiments results showed that the proposed compression algorithm has good compressive property. Compared with traditional approaches, the performance of our method corresponds to the conventional approach for smooth images. However, it was superior to others for images with rich texture and a large number of edge detail information. In the compression ration 4 : 1, the average peak signal to noise ratio of the proposed compression algorithmcould improve the average PSNR by 1.939 dB. They effectively protect the edge and texture detail information for multi-spectral image. It is very suitable for the application of space multi-spectral CCD image with rich texture and a large number of edge detail information.

[Lossless compression of hyperspectral image for space-borne application].

In order to resolve the difficulty in hardware implementation, lower compression ratio and time consuming for the whole hyperspectral image lossless compression algorithm based on the prediction, transform, vector quantization and their combination, a hyperspectral image lossless compression algorithm for space-borne application was proposed in the present paper. Firstly, intra-band prediction is used only for the first image along the spectral line using a median predictor. And inter- band prediction is applied to other band images. A two-step and bidirectional prediction algorithm is proposed for the inter-band prediction. In the first step prediction, a bidirectional and second order predictor proposed is used to obtain a prediction reference value. And a improved LUT prediction algorithm proposed is used to obtain four values of LUT prediction. Then the final prediction is obtained through comparison between them and the prediction reference. Finally, the verification experiments for the compression algorithm proposed using compression system test equipment of XX-X space hyperspectral camera were carried out. The experiment results showed that compression system can be fast and stable work. The average compression ratio reached 3.05 bpp. Compared with traditional approaches, the proposed method could improve the average compression ratio by 0.14-2.94 bpp. They effectively improve the lossless compression ratio and solve the difficulty of hardware implementation of the whole wavelet-based compression scheme.

[Application of ADV212 to the large field of view multi-spectral TDICCD space camera].

In order to resolve the difficult to hardware implementation of the whole vector quantization-based, predictive coding-based and transform coding-based compression scheme caused by the large volume of data, small spectral redundancy and line frequency variability etc. in multi-spectral space camera, an image compression scheme based on ADV212 was proposed in space camera. Firstly, custom-specific mode was presented based on the working principle of adv212 and CCD image properties. In the Custom-specific mode, an ADV212 is able to compress 8 channel CCD data by proposed image frame and interrupt processing strategy using pipelining. Then, this paper analyses the two parameters which affect the quality of image compression. A (10, 5) setting the quantization step method was proposed, This method makes the rate control error reduces 16.385%. Finally, the verification experiments to ADV212 compression system using ground test equipment were carried out. The experiments results showed that compression system can be fast and stable work. All PSNR were more than 30 dB in the compression ratio of 2:1 to 32:1. Compared with traditional approaches, the proposed method could improve the average PSNR by 2.49 dB in the lossy compression mode when the compression ratio is 1bpp. They effectively solve the difficult of hardware implementation of the whole wavelet-based compression scheme.

[Compression of interference hyperspectral image based on FHALS-NTD].

A hyperspectral interference image compression algorithm based on fast hierarchical alternating least squares nonnegative tensor Tucker decomposition (FHALS-NTD) is proposed. Firstly, the interference hyperspectral image is decomposed by 3-D OPD lifting-based discrete wavelet transform (3D OPT-LDWT) in the OPD direction. Then, the 3D DWT sub-bands decomposed are used as a three order nonnegative tensor, which is decomposed by the proposed FHALS-NTD algorithm to obtain 8 core tensors and 24 unknown component matrices. Finally, to obtain the final compressed bit-stream, each unknown component matrices element is quantized, and each core tensor is encoded by the proposed bit-plane coding of significant coefficients. The experimental results showed that the proposed compression algorithm could be used for reliable and stable work and has good compressive property. In the compression ratio range from 32 : 1 to 4 : 1, the average peak signal to noise ratio of proposed compression algorithm is higher than 40 dB. Compared with traditional approaches, the proposed method could improve the average PSNR by 1.23 dB. This effectively improves the compression performance of hyperspectral interference image.