Search

This work presents a prototype landslide early warning system (LEWS) adapted to real-time performance over the region of Catalonia (NE Spain). The system uses high-resolution rainfall information obtained from weather radar observations and susceptibility maps to issue a qualitative warning level at a regional scale. To study the influence of the mapping unit on the LEWS outputs, susceptibility maps obtained for Catalonia based on (i) pixels of different sizes and (ii) hydrological subbasins have been compared. The susceptibility has been derived using a simple fuzzy logic approach combining slope angle and land cover data. The susceptibility maps for the different mapping units have then been employed to run the LEWS for a period of 7 months (warm season of 2010). For each configuration, the performance, interpretability of the warnings, and computational requirements have been compared to assess the suitability of each mapping unit for their use in the LEWS in real time. The configuration using pixels of 30-m resolution as mapping units seems to be the best as a compromise between resolution, performance, and computational cost. However, from an end-user's real-time perspective, the interpretation of the warnings can be difficult. Therefore, summarizing and visualizing the warnings, which are computed over the high-resolution grid, by subbasins is proposed as the best option. [ABSTRACT FROM AUTHOR]

A novel and fast nonlocal mean (NLM) image denoising method using a structure tensor for Gaussian noise removal is proposed in this paper to address the difficult problem of the high computing cost of similarity weights. This method uses visual features based on fuzzy theory to measure the similarity between image pixels. First, from grayscale values and structure information in image patches, the similarity index measure is constructed by using a fuzzy theory measure. Then, the structure tensor is used to eliminate a large number of pixels of low similarity by the similarity index of the visual feature, which can improve the operation speed and avoid the filter parameters. Finally, NLM with a structure tensor and the similarity measure of fuzzy metrics is used to denoise the image. Due to the improved method considering the characteristics of the image visual structure, the structural similarity of NLM is improved. Compared with other improved NLM methods, the improved method in this paper has higher performance in denoising and reduces the complexity of the similarity computation and the problem of parameter setting. [ABSTRACT FROM AUTHOR]

Transform-based techniques partially address challenges like robustness and the imperceptibility in image steganography. Such approaches, however, increase the memory requirement and reduce the quality of the cover image and hiding capacity. Moreover, the steganography is always coupled with cryptography to strengthen the confidentiality. This paper presents an adaptive hybrid method for image steganography procedure based on bit reduction and pixel adjustment using the fuzzy logic and integer wavelet transform technique. The fuzzy set theory provides powerful tools to represent and process human knowledge in the form of fuzzy if-then rules that can resolve difficulties in image processing arising due to the uncertainty of the data, tasks, and results. We apply a bit reduction algorithm to each byte of the data which are to hide in the cover image. This decreases the memory usage and increases the capacity. The embedding of the input text into the cover image distorts the cover image. Hence, to minimize the visual difference between the cover image and the text embedded image, an optimum pixel adjustment algorithm is applied to the text embedded image. Simulation results demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concepts of stratified fuzzy soft topogenous, stratified fuzzy soft filter, stratified fuzzy soft quasi-proximity and stratified fuzzy soft grill. Also, we introduce the concept of fuzzy soft topogenous structures by combining fuzzy soft topogenous with fuzzy soft filter and we introduce the concept of fuzzy soft quasi-proximity by combining fuzzy soft quasi-proximity with fuzzy soft grill and give their properties. Furthermore, we establish the relationship among these fuzzy soft topological structures and their stratifications. [ABSTRACT FROM AUTHOR]

Impulse noise, also known as impulsive noise, is one of the most common types of noise occurring in digital images. The median filter and morphological filters are often used to remove impulsive noise, but these filters do not preserve image details. In this paper, we first apply the median filter in order to reduce the amount of impulsive noise in a corrupted image. After an application of the direct fuzzy transform (FT) to the resulting image, we restored the pixel values corresponding to locations flagged by the fuzzy rule-based noise detector by means of the inverse fuzzy transform. Finally, we obtained the output of our proposed image filter by combining the restored pixels with the ones marked as noiseless by the aforementioned noise fuzzy detector. We compared the results of our approach that we called adaptive FT-based image filter (AFT-IF) with the ones obtained by a number of other image filters for impulsive noise reduction. [ABSTRACT FROM AUTHOR]

Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. [ABSTRACT FROM AUTHOR]

This paper presents a new wavelet-based method for fusion of spatially registered multi-focus images. We have formulated the image fusion process as a two-class classification problem: in and out-of-focus classes. First, a 12-dimensional feature vector using dual-tree discrete wavelet transform (DT-DWT) sub-bands of the source images are extracted, and then a trained two-class fisher classifier projects it to the class labels. The classifier output is used as a decision map for fusing high-frequency wavelet coefficients of multi-focus source images in different directions and decomposition levels of the DT-DWT. In addition, there is an uncertainty for selecting high-frequency wavelet coefficients in smooth regions of source images, which causes some misclassified pixels in the classification output or the decision map. In order to solve this uncertainty and integrate as much information as possible from the source images into the fused image, we propose an algorithm based on fuzzy logic, which combines outputs of two different fusion rules based on a dissimilarity measure from the source images: Selection based on the decision map and weighted averaging. An estimation of the decision map is also used for fusing low-frequency wavelet coefficients of the source images instead of simple averaging. After fusing low- and high-frequency wavelet coefficients of the source images, the final fused image is obtained using the inverse DT-DWT. This new method provides improved subjective and objectives results (more than 4.5 dB on average) as compared to previous fusion methods. [ABSTRACT FROM AUTHOR]