Deep invariant texture features for water image classification

Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy

Image-based material analysis of ancient historical documents

Researchers continually perform corroborative tests to classify ancient historical documents based on the physical materials of their writing surfaces. However, these tests, often performed on-site, requires actual access to the manuscript objects. The procedures involve a considerable amount of time and cost, and can damage the manuscripts. Developing a technique to classify such documents using only digital images can be very useful and efficient. In order to tackle this problem, this study uses images of a famous historical collection, the Dead Sea Scrolls, to propose a novel method to classify the materials of the manuscripts. The proposed classifier uses the two-dimensional Fourier Transform to identify patterns within the manuscript surfaces. Combining a binary classification system employing the transform with a majority voting process is shown to be effective for this classification task. This pilot study shows a successful classification percentage of up to 97% for a confined amount of manuscripts produced from either parchment or papyrus material. Feature vectors based on Fourier-space grid representation outperformed a concentric Fourier-space format.Comment: 8 pages, 11 figures including supplementary documents; Submitted to ICPR 202

An entropy-based analysis of GPR data for the assessment of railway ballast conditions

The effective monitoring of ballasted railway track beds is fundamental for maintaining safe operational conditions of railways and lowering maintenance costs. Railway ballast can be damaged over time by the breakdown of aggregates or by the upward migration of fine clay particles from the foundation, along with capillary water. This may cause critical track settlements. To that effect, early stage detection of fouling is of paramount importance. Within this context, ground penetrating radar (GPR) is a rapid nondestructive testing technique, which is being increasingly used for the assessment and health monitoring of railway track substructures. In this paper, we propose a novel and efficient signal processing approach based on entropy analysis, which was applied to GPR data for the assessment of the railway ballast conditions and the detection of fouling. In order to recreate a real-life scenario within the context of railway structures, four different ballast/pollutant mixes were introduced, ranging from clean to highly fouled ballast. GPR systems equipped with two different antennas, ground-coupled (600 and 1600 MHz) and air-coupled (1000 and 2000 MHz), were used for testing purposes. The proposed methodology aims at rapidly identifying distinctive areas of interest related to fouling, thereby lowering significantly the amount of data to be processed and the time required for specialist data processing. Prominent information on the use of suitable frequencies of investigation from the investigated set, as well as the relevant probability values of detection and false alarm, is provided

Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the hyperspectral image denoising: A progressively multiscale information aggregation network and a co-attention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a co-attention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capability for denoising. Extensive experiments on synthetic and real HSI datasets demonstrate that the proposed MAFNet has achieved better denoising performance than other state-of-the-art techniques. Our codes are available at \verb'https://github.com/summitgao/MAFNet'.Comment: IEEE JSTASRS 2023, code at: https://github.com/summitgao/MAFNe

Study of air pollutant detection by remote sensors

Air pollution detection using satellite observatio