The advances in remote sensing sensors during the last two decades have led to the production of very high spatial resolution multispectral images. In order to adapt to this rapid development and handle these data, object-based analysis has emerged. A critical part of such an analysis is image segmentation. The selection of optimal segmentation parameters' values generates a qualitative segmentation output and has a direct impact on feature extraction and subsequent overall classification accuracy. Even though several image segmentation methods have been developed and suggested in the literature, each of them has advantages and disadvantages. This article presents the conceptual characteristics of image segmentation methods with a special focus on semantic segmentation. In addition, a meta-analysis was conducted through a comprehensive review of recent image segmentation case studies. It includes statistics and quantitative data regarding the applied segmentation algorithm, the software utilized and the data source among others. Since there is no miraculous segmentation algorithm, the statistical results depict only the recent trend. Finally, a few interesting subjects are addressed, including identification of current problems, image segmentation on non-traditional data and hot topics for future research.

Remote sensing image segmentation advances: A meta-analysis

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

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Social Network Search for Solving Engineering Optimization Problems.

The ubiquity of mobile camera devices has been triggering an outcry of privacy concerns, whereas privacy protection still relies on the cooperation of the photographer or camera hardware, which can hardly be guaranteed in practice. In this paper, we introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in specialized waveforms. We use a model-driven approach to optimize the waveform, so as to ensure protection against the (uncontrollable) cameras and potential image-processing based attacks. We have also designed mechanisms to unblock authorized cameras and enable graceful degradation under strong ambient light interference. Our prototype implementation and experiments show that LiShield can effectively destroy unauthorized capturing while maintaining robustness against potential attacks.

https://dl.acm.org/doi/pdf/10.1145/3117811.3117820

Automating Visual Privacy Protection Using a Smart LED

With the development and popularization of the Internet and the rise of various live broadcast platforms, digital videos have penetrated into all aspects of people’s life. At the same time, all kinds of pirated videos are also flooding the Internet, which seriously infringe the rights and interests of video copyright owners and hinder the healthy development of the video industry. Therefore, robust video watermarking algorithms for copyright protection have emerged as these times require. In this paper, we review robust video watermarking algorithms for copyright protection based on original videos and compressed videos. Basic models and properties of video watermarking algorithms are described, and the evaluation indexes corresponding to each property are also introduced. To help researchers understand various existing robust watermarking algorithms quickly, some basic information and the quantitative estimation of several performances are analyzed and compared. Finally, we discuss the challenges in the research of robust video watermarking algorithms, and give possible development directions for the future.

https://www.mdpi.com/2076-3417/8/10/1891/pdf

A Survey on Robust Video Watermarking Algorithms for Copyright Protection

Summary Nowadays, with the rapid growth of technologies and the prevalence of network communication, new challenges raises to protect the multimedia data transmission on the World Wide Web (internet). Data security is one of the main important needs these days, especially in the field of remote sensing. Emirates Institution for Advanced Science and Technology (EIAST) as an example provides a satellite images captured by DubaiSat-1 satellite to customers inside the UAE including government agencies, universities, research centers, and distributed also outside the region. To deal with the issue of data security, the concept of watermarking has been introduced. The idea of watermarking is to embed a secret message, watermark inside an image and to increase the digital data security. The aim of this study is to hide an entire pattern as a watermark such as an organization’s logo (or trademark) directly into the original DubaiSat-1 satellite image. This will prevent any unauthorized manipulation to the image data. This secret watermark is used for copyright protection and ownership authentication for (EIAST). This study will be based on Discrete Cosine Transform (DCT) to provide an excellent protection and highly robust under flipping, noise, resize and rotation attacks.

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Robust Watermarking Technique based on DCT to Protect the Ownership of DubaiSat-1 Images against Attacks

Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the F1-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated F1-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% and 87.03%, date palm class-specific accuracies of 0.91 and 0.89, and kappa coefficients of 0.90 and 0.85 for the first and second study areas, respectively. The proposed integrated methodology demonstrated a highly efficient and promising tool to detect and map date palm trees from VHSR aerial images.

Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

This paper proposes a semantic segmentation technique to automatically detect water-bodies from DubaiSat-2 images. The proposed method uses a deep convolutional neural network transfer-learning model. Several evaluation metrics such as accuracy, precision, and Jaccard coefficient are used to test our proposed algorithm. The overall accuracy for the prediction of water-bodies in DubaiSat-2 image dataset is 99.86%.

Detection of Water-Bodies Using Semantic Segmentation

In this paper, we propose a new blind colour video watermarking technique for the copyright protection of multimedia colour videos using index mapping concept. The novelty in the presented approach consists in designing a hybrid Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) based digital video watermarking of colour watermark logo using index mapping technique. The distortion caused by watermarking is assessed by using peak signal to noise ratio (PSNR) and similarity structure index measure (SSIM) and robustness against different types of attacks have been assessed using StirMark. The proposed video watermarking algorithm provides better imperceptibility in harmony with the human visual system and offers higher robustness against signal processing attacks.

Saeed Al-Mansoori

Papers

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Robust Watermarking Technique based on DCT to Protect the Ownership of DubaiSat-1 Images against Attacks

Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

Detection of Water-Bodies Using Semantic Segmentation

Index mapping based hybrid DWT-DCT watermarking technique for copyright protection of videos files