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Classification is one of the most popular topics in remote sensing. Consider the problems that the remote sensing data are complicated and few labeled training samples limit the performance and efficiency in the classification of remote sensing image. For these problems, a huge number of methods were proposed in the last two decades. However, most of them do not yield good performance. In this paper,...
In this paper, we proposed an efficient multiresolution decomposition framework for iris texture feature extraction, which has superior directionality and localized frequency partition. It consists of an improved circular symmetric filter bank followed by a directional filter bank. In our iris recognition system, we adopt a novel non-polar coordinate normalization strategy as iris preprocessing method...
In this paper, a novel approach is proposed to solve the pansharpening problem for remote sensing images. Using this approach, lower-resolution multispectral images are fused with the higher-resolution panchromatic image for the purpose of spatial resolution enhancement. To achieve better spectral quality for the spatially enhanced multispectral images, the fusion parameters are optimized using the...
A two-step approach to enhance the resolution of remote sensing thermal infrared (TIR) images is proposed in this paper. For difference in imaging principles between TIR image and optical images, traditional image fusion techniques, such as component substation and MRA methods will not be proper. In our study, we use extreme learning machine (ELM) to regress the relationship between TIR image and...
In this paper, we propose a new computer aided diagnosis method for prostate cancer detection in ultrasound image. With multi resolution autocorrelation texture features and clinical features such as location and shape of tumor, we could maintain high specificity with high sensitivity for prostate cancer detection. Multi resolution autocorrelation can detect cancer suspicious region efficiently with...
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