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.
Proc. SPIE. 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
KEYWORDS: Hyperspectral imaging, Principal component analysis, Image compression, Image processing, Remote sensing, Error analysis, Composites, Data acquisition, Data conversion, Spectral models
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.