Enhancing the Binary Watermark-Based Data Hiding Scheme Using an Interpolation-Based Approach for Optical Remote Sensing Images

Author(s):  
Mohammad Reza Khosravi ◽  
Habib Rostami ◽  
Sadegh Samadi

Computer networking and internet developments create new challenges in information security and copyright protection. In order to protect the multimedia data and also in some cases, for information management, the authors can use watermarking schemes to achieve more security. In this article, the authors firstly review a watermarking scheme for remote sensing applications, represented by Zhu et al.; They also explain two problems in Zhu et al.'s scheme and in addition, introduce two solutions for these problems. Generally, Zhu et al.'s scheme is a non-blind scheme that also does not have any attention to watermarked image quality, thus they try to represent ways in order to achieve the blind and quality-aware watermarking. Experimental results confirm that both of their modifications have suitable effectiveness than the original scheme whereas in practice, their modifications create an output with embedding capacity like the original scheme but it is high quality and also blind.

Author(s):  
Mohammad Reza Khosravi ◽  
Habib Rostami ◽  
Sadegh Samadi

Computer networking and internet developments create new challenges in information security and copyright protection. In order to protect the multimedia data and also in some cases, for information management, the authors can use watermarking schemes to achieve more security. In this article, the authors firstly review a watermarking scheme for remote sensing applications, represented by Zhu et al.; They also explain two problems in Zhu et al.'s scheme and in addition, introduce two solutions for these problems. Generally, Zhu et al.'s scheme is a non-blind scheme that also does not have any attention to watermarked image quality, thus they try to represent ways in order to achieve the blind and quality-aware watermarking. Experimental results confirm that both of their modifications have suitable effectiveness than the original scheme whereas in practice, their modifications create an output with embedding capacity like the original scheme but it is high quality and also blind.


2021 ◽  
Vol 3 ◽  
pp. 100019
Author(s):  
Alvarez-Vanhard Emilien ◽  
Corpetti Thomas ◽  
Houet Thomas

Author(s):  
Hessah Albanwan ◽  
Rongjun Qin

Remote sensing images and techniques are powerful tools to investigate earth’s surface. Data quality is the key to enhance remote sensing applications and obtaining clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works.


2002 ◽  
Vol 8 (1) ◽  
pp. 36-47 ◽  
Author(s):  
A. HALL ◽  
D.W. LAMB ◽  
B. HOLZAPFEL ◽  
J. LOUIS

2021 ◽  
Vol 17 (3) ◽  
pp. 235-247
Author(s):  
Jun Zhang ◽  
Junjun Liu

Remote sensing is an indispensable technical way for monitoring earth resources and environmental changes. However, optical remote sensing images often contain a large number of cloud, especially in tropical rain forest areas, make it difficult to obtain completely cloud-free remote sensing images. Therefore, accurate cloud detection is of great research value for optical remote sensing applications. In this paper, we propose a saliency model-oriented convolution neural network for cloud detection in remote sensing images. Firstly, we adopt Kernel Principal Component Analysis (KCPA) to unsupervised pre-training the network. Secondly, small labeled samples are used to fine-tune the network structure. And, remote sensing images are performed with super-pixel approach before cloud detection to eliminate the irrelevant backgrounds and non-clouds object. Thirdly, the image blocks are input into the trained convolutional neural network (CNN) for cloud detection. Meanwhile, the segmented image will be recovered. Fourth, we fuse the detected result with the saliency map of raw image to further improve the accuracy of detection result. Experiments show that the proposed method can accurately detect cloud. Compared to other state-of-the-art cloud detection method, the new method has better robustness.


2020 ◽  
Author(s):  
Caio Cesar Viana Da Silva ◽  
Jefersson Alex Dos Santos

The development of computational vision approaches that exploit satellite imagery is relatively recent, mainly due to the limited availability of this type of image. In the area of remote sensing, applications that employ computational vision techniques are modeled for classification in closed set scenarios. However, the world is not purely closed set, many scenarios present classes that are not previously known by the algorithm, an open set scenario. Thus, the main objective of this paper is the study and development of semantic segmentation techniques considering the open set scenario applied to remote sensing images. Focusing on this problem, this is the first work to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of four novel approaches for open set semantic segmentation. Our methods yielded competitive results when compared to closed set methods for the same dataset


2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Pengwei Li ◽  
Wenying Ge

Shadows limit many remote sensing applications such as classification, target detection, and change detection. Most current shadow detection methods utilize the histogram threshold of spectral characteristics to distinguish the shadows and nonshadows directly, called “hard binary shadow.” Obviously, the performance of threshold-based methods heavily rely on the selected threshold. Simultaneously, these threshold-based methods do not take any spatial information into account. To overcome these shortcomings, a soft shadow description method is developed by introducing the concept of opacity into shadow detection, and MRF-based shadow detection method is proposed in order to make use of neighborhood information. Experiments on remote sensing images have shown that the proposed method can obtain more accurate detection results.


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