An adaptive algorithm of two-dimension secret information hiding for remote sensing image

2005 ◽  
Author(s):  
Xianmin Wang ◽  
Zequn Guan ◽  
Chenhan Wu
Keyword(s):  
Remote Sensing ◽  
Adaptive Algorithm ◽  
Information Hiding ◽  
Remote Sensing Image ◽  
Two Dimension ◽  
Secret Information

A Steganography Embedding Method Based on Hamming Coding and Histogram-Preserving

2019 ◽  
Vol 33 (14) ◽  
pp. 1954039
Author(s):  
Ya-Feng Li ◽  
Ren-Er Yang ◽  
Jie Cheng ◽  
Hong-Zhu Dai
Keyword(s):  
Digital Media ◽  
Adaptive Algorithm ◽  
Information Hiding ◽  
Entropy Analysis ◽  
Hamming Codes ◽  
Secret Information ◽  
Matrix Embedding ◽  
Before And After ◽  
Histogram Preserving ◽  
Break Even Point

This paper proposes an information hiding algorithm using matrix embedding with Hamming codes and histogram preservation in order to keep the histogram of the image unchanged before and after hiding information in digital media. First, the algorithm uses matrix embedding with Hamming codes to determine the rewriting bits of the original image, rewrite and flip them, and successfully embed the secret information. Then, according to the idea of a break-even point, a balanced pixel frequency adaptive algorithm is proposed and each embedded bit of secret information is detected and compensated by the adjacent bit of histogram data, so that the histogram change of the image before and after information hiding is minimized. At present, most of the histogram distortion values after steganography are generally over 1000 or even higher. As a contrast, the method proposed in this paper can keep the histogram distortion values to be less than 1000. The feasibility and effectiveness of the algorithm are verified by relative entropy analysis as well. The experimental results also show that the algorithm performs well in steganographic analyses of images.

Secret spatial information hiding technique for remote sensing image

2007 ◽  
Author(s):  
Xianmin Wang ◽  
Cheng Wang ◽  
Jianzhong Zhou ◽  
Yongchuan Zhang
Keyword(s):  
Remote Sensing ◽  
Information Hiding ◽  
Spatial Information ◽  
Remote Sensing Image

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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