Hyperspectral Image Resolution Enhancement And Its Application On Small Target Detection

Hyperspectral remote sensing is a technique based on the spectroscopy, which contains abundant spectral information besides the spatial information of the images, and overcomes the limitations of the wide-band remote sensing detection. As a result, it has been widely used in many areas and becomes one of the most important earth observation information source. However, with the limitations such as imaging spectrometer manufacturing techniques and imaging principles, the spatial resolution of the hyperspectral images is relatively low. This brings a series of problems to the further applications, such as detection of the specific targets. Therefore, in this thesis, the methods of information fusion and mixed pixel decomposition have been researched to enhancement the image spatial resolution, and finally to improve the capability of the hyperspectral image target detection combining the spatial and spectral information.Firstly, this thesis introduces the principle of electromagnetic wave, analyses the relationship between spatial resolution and spectral resolution. For the same CCD, the spatial resolution is decreasing with the increase of spectral resolution. This thesis also introduces the characteristic of hyperspectral image, analyses the characteristic and applicable range of several dimension reduction algorithms, such as PCA, MNF and LDA. Dimension reduction is an important image preprocessing technique which lays the foundation for the subsequent work.And then, this thesis introduces the widely used target detection algorithms, analyses the support vector data description(SVDD) and its classification capability and applicable range. Aiming at the unbalanced problem between the target and background samples in most traditional pure-pixel target detection algorithms, a hyperspectral image target detection algorithm based on SVDD has been proposed in this thesis, which converts target detection to one-class classification problem. Experimental results show that compared with the traditional algorithms such as spectral angle mapping and constrained energy minimization, this algorithm needs less target training samples and achieves approximately the same results as these two methods, when increasing the background samples, this method is superior to those two ones.Aiming at low spatial resolution of hyperspectral image, by means of spatial information compensation, a data fusion algorithm of enhancing hyperspectral image resolution based on relevance vector machine(RVM) is proposed in this thesis. This algorithm needs the information from other source, so the registration techniques between multi-images is firstly studied. An algorithm of registration control point extraction is proposed, which based on the Gaussian fitting and a RVM geometric model, and then a more precise registration result can be obtained. In terms of supplementary information compensation method, a fusion algorithm based on RVM is proposed, which aims at enhancing the spatial information of the hyperspectral images, and at the same time maintaining the original spectral characteristics. When applying the enhanced images to a pure-pixel small target detection, the result showed that the proposed method can solve the problem of poor detection results caused by the low resolution of the hyperspectral images. The detection precision of the enhanced resolution image is much better than the original one.In the condition of lack of assistant information, spectral unmixing techniques has been researched in order to enhance the spatial resolution of the hyperspectral images and solve the spectral mixing problems. Mixing pixel decomposition includes two key steps: endmember extraction and abundance solving. Due to the noise sensitivity for the spectral endmember extraction in the original N-FINDR algorithm, a new endmember extraction algorithm is proposed on the basis of unsupervised clustering algorithm. This algorithm utilizes K-means clustering to select a representative spectrum from hyperspectral data, and then select the endmember from these spectra. Experimental results showed that the algorithm has a strong anti-noise performance. On the conditions that the existence of some unknown features contained in many pixels, unmixing results on these points will have a large deviation. An unmixing method for hyperspectral image based on SVDD is proposed. It firstly divides the hyperspectral data into two categories by SVDD, one is the mixed pixels with known materials, and the other contains unknown materials. The boundaries between them are considered to be a mixed pixel of the known and unknown ground covers. And these pixels are then decomposed. Experimental results show that the algorithm can effectively solve the low unmixing precision problem caused by the unknown pixels, and can give the mixing factor for the unknown pixels. Based on the high accurate unmixing fraction map, a sub-pixel mapping technique for interested targets is adopted to improve the spatial resolution. The experimental results show that the method can preserve the target shape better, and the targets in the processed image are prone to be detected by combining the spatial and spectral information.