This paper proposed an infrared hyperspectral band selection algorithm based on autoencoder Combining neural network, deep learning and other methods, an infrared hyperspectral band selection algorithm based on autoencoder is proposed to reduce the dimension of infrared hyperspectral images without loss of information. Encode infrared hyperspectral data to obtain dimensionality reduced data, decode the dimensionality reduced data to obtain reconstructed hyperspectral data, and use a band selection evaluation method based on average reconstruction error to evaluate the effectiveness of this band selection method. Based on the measured infrared hyperspectral data, the performance of this algorithm is compared with that of the band selection algorithm based on spatial dimension inter class separability and spectral dimension inter class separability. Experimental results have shown that the algorithm proposed in this paper outperforms the other two algorithms and has low reconstruction error in band selection results.
KEYWORDS: Solar radiation models, Solar radiation, Radiative energy transfer, Sun, Process modeling, Heat flux, Thermal modeling, 3D acquisition, Infrared radiation
Regarding the radiation relationship between complex objects involved in the space environment, in this paper we use discrete coordinate method to calculate and analyze the coupled radiation-conduction heat transfer between complex objects. We simulate the heat conduction process by use of the finite volume method, and the overall temperature field is finally obtained. By comparing the calculation results of the program and Fluent software, the correctness of the coupled radiation-conduction heat transfer program is verified. The result shows that the radiation heat transfer between the targets could affect the temperature field under the conditions of self-emission and ambient radiation. And it also shows that an appropriate reduction of discrete points can reduce the calculation time and will not affect the calculation accuracy.
Infrared point source target recognition is the target recognition technology based on non-imaging time-domain signal characteristics of infrared detectors for small size or long distance targets. It has wide application prospects in many fields, such as industry and national defense. However, infrared point source targets recognition has become a recognized problem, because this kind of targets has less information and more interference factors. Therefore, the research on infrared point source target recognition has important theoretical and practical value. This paper proposed a mathematical construction method of infrared point source target signal .And through the modeling of sensor response and the verification of hardware-in-the-loop simulation experiment, a set of time-domain signal data for infrared point source target recognition method is generated. Then, by extracting several time–domain features, a nearest neighbor classification method based on feature weighting is proposed by combining the idea of mathematical model matching. At the same time, the influence of the uncertainty of the model itself on the recognition effect is considered. The result shows, using the sample data of point source target generated by simulation, the best recognition time of proposed methods is approximately 6-8 seconds, and the recognition accuracy rate is 64.6%-79.2%.
This paper proposed a hyperspectral subpixel target detection algorithm based on joint spectral and spatial preprocessing prior to endmember extraction and spectral angle mapping(SAM). Under the condition that the prior information of targets and background is unknown, the spectral and spatial information is used to locate and detect targets. Then we can make hyperspectral subpixel targets detected and recognised. The joint spectral and spatial preprocessing prior to endmember extraction method is performed to extract endmembers. The spectral angle mapping method is used to detect and recognize the interested targets. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with SAM algorithm and RX algorithm by a specifically designed experiment. From the results of the experiments, it is illuminated that the proposed algorithm can detect subpixel targets with lower false alarm rate and its performance is better than that of the other algorithms under the same condition.
A temperature controllable canister for protecting IR camera in low temperature and vacuum environment is experimentally and theoretically studied. The simulation of thermal transport model is analyzed,and the simulation results of four situations explain that this canister’s heating power dissipation has a capability that it can keep the temperature of IR above -40 centigrade. And the heater of the canister can keep the IR working at the temperature above 30 centigrade. The temperature control is achieved in low temperature and vacuum environment by using this technique, which has been validated by a experiment operated at the space environment simulator.
This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm’s performance is better than that of the other algorithms under the same condition and has higher classification accuracy.
Due to the high hyperspectral data volume, high dimensionality and the data itself having great redundancy, the accuracy of Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm is low. In view of this, we proposed an endmember extraction algorithm based on PCA-SMACC. First , it uses principal component analysis(PCA)algorithm to achieve the purpose of hyperspectral data dimensionality reduction. The method removes the data redundancy while maintains the validity of the data. Then it uses SMACC endmember extraction algorithm on the resulting principal component images. The experimental results show that PCA-SMACC algorithm can compensate for the lack of traditional algorithms. Compared with PPI and SMACC algorithms, PCA-SMACC has improved to some extent in the extraction accuracy and speed.
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