Concerning the difficult acquisition of training data in HyperSpectral Image(HSI),a new semi-supervised classification framework for HSI was adopted,in which both limited labeled data and abundant unlabeled data were used to train deep neural networks. At the same time,the unbalanced distribution of hyperspectral samples leads to huge differences in the classification difficulty of different samples,and the original cross-entropy loss function is unable to describe this distribution feature,so the classification effect is not ideal. To address this problem,a multi-classification objective function based on focal loss was proposed in the semi-supervised classification framework. Finally,considering the influence of spatial information of HSI on classification,combined with Markov Random Field(MRF),the sample space features were used to further improve the classification effect. The proposed method was compared with various classical methods on two commonly used HSI datasets. Experimental results show that the proposed method can obtain classification results superior to other comparison methods.

Aiming at not satisfactory defogging effect of the traditional defogging algorithm based on polarized characteristics in heavy fog, a new color space conversion algorithm using dark channel prior for polarization image dehazing was proposed. Compared with the traditional imaging technology, polarization imaging detection technology has remarkable advantages in the target detection and recognition of complex environment. Intensity, polarization degree and polarization angel information are usually used to describe target's polarization information for polarization images. In order to combine the polarization information and defogging model, a method of color space transformation was adopted. Firstly, the polarization information was converted into the components of the brightness, hue, saturation in Hue-Intensity-Saturation (HIS) color space and then the HIS color space was mapped to the Red-Green-Blue (RGB) space. Secondly, the dark channel prior principle was applied to get the dark channel image with the combination of the atmospheric scattering model in haze weather. Finally, the atmospheric transmission rate was elaborated by using softmatting algorithm based on sparse prior of the image. The experimental results show that, compared with the existing polarization defogging algorithm, many technical specifications of defogged images such as standard deviation, entropy, average gradient of the proposed algorithm have been greatly improved in very low visibility conditions. The proposed algorithm can effectively enhance the global contrast in heavy fog weather and improve the identification capability for the polarized images.

Because the algorithm of cloud threshold often generates boundary effect, this paper proposed an improved algorithm based on wavelet transform and homomorphic filter. The image with cloud was decomposed by wavelet transform to find the proper number of demarcation levels. Cloud could be removed by making homomorphic filtering to the higher level coefficients, while giving the lower level detailed coefficients and the approximation coefficients some weight factors respectively. The three parts of coefficients were reconstructed and fused to get processed result. The experimental results indicate that the proposed algorithm can remove the thin cloud cover effectively, maintain the details better and prevent producing the boundaries.