Markov random field (MRF) model is an effective tool for polarimetric synthetic aperture radar (PolSAR) image classification. However, due to the lack of suitable contextual information in conventional MRF methods, there is usually a contradiction between edge preservation and region homogeneity in the classification result. To preserve edge details and obtain homogeneous regions simultaneously, an adaptive MRF framework is proposed based on a polarimetric sketch map. The polarimetric sketch map can provide the edge positions and edge directions in detail, which can guide the selection of neighborhood structures. Specifically, the polarimetric sketch map is extracted to partition a PolSAR image into structural and nonstructural parts, and then adaptive neighborhoods are learned for two parts. For structural areas, geometric weighted neighborhood structures are constructed to preserve image details. For nonstructural areas, the maximum homogeneous regions are obtained to improve the region homogeneity. Experiments are taken on both the simulated and real PolSAR data, and the experimental results illustrate that the proposed method can obtain better performance on both region homogeneity and edge preservation than the state-of-the-art methods.
We present a novel fusion method to improve the spatial resolution of multispectral (MS) images, where the fused spectral images integrate the spectral information and spatial details from the original MS images and panchromatic (PAN) image, respectively. Band by band, a spectral image with high resolution is reconstructed from the original spectral image to take advantage of super-resolution technology. The pan-sharpening method via Amélioration de la Resolution Spatiale par Injection de Structures concept is further applied to obtain the fused images from the reconstructed spectral images and PAN image. Performance of the proposed method has been evaluated on the public optical satellite QuickBird images. Experimental results show that the fused spectral images both preserve the spatial details of high-resolution from the PAN image and have higher spectral resolution than the original spectral images.
Synthetic aperture radar (SAR) images compression is very important in reducing the burden of data storage and
transmission. Finding efficient geometric representations of images is a central issue in improving the efficiency of
image compression. Bandelet provides an efficient way for image representation based on geometric regularity. In the
second generation Bandelet, the multiscale decomposition of image is completed by 2D wavelet transform (WT) and the
obtained subbands images are squared partitioned. Then a bottom to top CART algorithm is used to prune the quadtree,
and finally an exhaustive searching algorithm is used to obtain the optimal direction in each square. This process is of
high complexity in time and space though it can provide an efficient representation of images than WT. Considering this,
we proposed a rapid implementation of Bandelet transform based on fixed size image partition, and then applied it to
SAR image compression. Experiments results show that in relative to the second generation Bandelets, our proposed
method has rapid implementation and comparable performance with chinalake and abq_apt in 0.5-2.0bpp. An
improvement of PSNR(Peak Signal to Noise Ratio) and the preservation of edges and texture over JPEG2000 are