scholarly journals Entropy-Based Semi-Fragile Watermarking of Remote Sensing Images in the Wavelet Domain

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 847 ◽  
Author(s):  
Jordi Serra-Ruiz ◽  
Amna Qureshi ◽  
David Megías
Keyword(s):  
Remote Sensing ◽  
Three Dimensional ◽  
Low Frequency ◽  
Rate Distortion ◽  
Discrete Wavelet ◽  
Remote Sensing Images ◽  
Vector Quantizer ◽  
Jpeg2000 Compression ◽  
Optimal Value ◽  
Image Tampering Detection

This article presents a semi-fragile image tampering detection method for multi-band images. In the proposed scheme, a mark is embedded into remote sensing images, which have multiple frequential values for each pixel, applying tree-structured vector quantization. The mark is not embedded into each frequency band separately, but all the spectral values (known as signature) are used. The mark is embedded in the signature as a means to detect if the original image has been forged. The image is partitioned into three-dimensional blocks with varying sizes. The size of these blocks and the embedded mark is determined by the entropy of each region. The image blocks contain areas that have similar pixel values and represent smooth regions in multispectral or hyperspectral images. Each block is first transformed using the discrete wavelet transform. Then, a tree-structured vector quantizer (TSVQ) is constructed from the low-frequency region of each block. An iterative algorithm is applied to the generated trees until the resulting tree fulfils a requisite criterion. More precisely, the TSVQ tree that matches a particular value of entropy and provides a near-optimal value according to Shannon’s rate-distortion function is selected. The proposed method is shown to be able to preserve the embedded mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their positions in the whole image. Experimental results show how the scheme can be applied to detect forgery attacks, and JPEG2000 compression of the images can be applied without removing the authentication mark. The scheme is also compared to other works in the literature.

Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

2014 ◽  
Vol 12 (05) ◽  
pp. 1461010 ◽  
Author(s):  
Changmiao Hu ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Low Frequency ◽  
Median Filtering ◽  
Remote Sensing Images ◽  
Infrared Band ◽  
Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

scholarly journals Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

2020 ◽  
Vol 9 (7) ◽  
pp. 462
Author(s):  
Josephina Paul ◽  
B. Uma Shankar ◽  
Balaram Bhattacharyya
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Feature Fusion ◽  
Spectral Band ◽  
Minimum Energy ◽  
Low Frequency ◽  
Remote Sensing Images ◽  
Difference Image ◽  
Difference Images

Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

Watermark-based Copyright Protection Using Invariant Features for Remote Sensing Images

2020 ◽  
Author(s):  
Na Ren ◽  
Changqing Zhu
Keyword(s):  
Remote Sensing ◽  
Copyright Protection ◽  
Low Frequency ◽  
Image Features ◽  
Remote Sensing Images ◽  
Processing Theory ◽  
Invariant Features ◽  
Original Host ◽  
Sensing Technology ◽  
Feature Processing

<p>With the development of remote sensing technology, the copyright protection of remote sensing images has become an urgent problem to be solved. In this paper, a blind watermarking scheme based on invariant features is applied. In the embedding process, the stable image features are firstly extracted from the original host using block DCT, and the embedding positions are constructed adaptively according to feature processing theory. Then, the watermark is embedded into the low-frequency coefficients by modifying the DC coefficients. For watermark extraction, according to the invariant image features in each region, the watermark location and the watermark information can be extracted without the original host. Experimental results show that the proposed watermarking is not only invisible and robust against common image processing, such as noise addition, image filtering, and JPEG compression, but also robust against cropping attack.</p>

Fusing Remote Sensing Images Using a Statistical Model

2012 ◽  
Vol 263-266 ◽  
pp. 416-420 ◽  
Author(s):  
Xiao Qing Luo ◽  
Xiao Jun Wu
Keyword(s):  
Remote Sensing ◽  
Statistical Model ◽  
Image Fusion ◽  
Spatial Information ◽  
Low Frequency ◽  
Principal Component ◽  
Remote Sensing Images ◽  
Entropy Information ◽  
Remote Sensing Image Fusion ◽  
The One

Enhance spectral fusion quality is the one of most significant targets in the field of remote sensing image fusion. In this paper, a statistical model based fusion method is proposed, which is the improved method for fusing remote sensing images on the basis of the framework of Principal Component Analysis(PCA) and wavelet decomposition-based image fusion. PCA is applied to the source images. In order to retain the entropy information of data, we select the principal component axes based on entropy contribution(ECA). The first entropy component and panchromatic image(PAN) are performed a multiresolution decompositon using wavelet transform. The low frequency subband fused by weighted aggregation approach and high frequency subband fused by statistical model. High resolution multispectral image is then obtained by an inverse wavelet and ECA transform. The experimental results demonstrate that the proposed method can retain the spectral information and spatial information in the fusion of PAN and multi-spectral image(MS).

scholarly journals Improved WαSH Feature Matching Based on 2D-DWT for Stereo Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3494 ◽  
Author(s):  
Mei Yu ◽  
Kazhong Deng ◽  
Huachao Yang ◽  
Changbiao Qin
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Feature Matching ◽  
Discrete Wavelet ◽  
Remote Sensing Images ◽  
Illumination Change ◽  
Affine Deformation ◽  
Local Invariant Features ◽  
Image Pairs ◽  
Outstanding Issue

Image matching is an outstanding issue because of the existing of geometric and radiometric distortion in stereo remote sensing images. Weighted α-shape (WαSH) local invariant features are tolerant to image rotation, scale change, affine deformation, illumination change, and blurring. However, since the number of WαSH features is small, it is difficult to get enough matches to estimate the satisfactory homography matrix or fundamental matrix. In addition, the WαSH detector is extremely sensitive to image noise because it is built on sampled edges. Considering the shortcomings of the WαSH detector, this paper improves the WαSH feature matching method based on the 2D discrete wavelet transform (2D-DWT). The method firstly performs 2D-DWT on the image, and then detects WαSH features on the transformed images. According to the methods of descriptor construction for WαSH features, three matching methods on the basis of wavelet transform WαSH features (WWF), improved wavelet transform WαSH features (IWWF), and layered IWWF (LIWWF) are distinguished with respect to the character of the sub-images. The experimental results on the dataset containing affine distortion, scale distortion, illumination change, and noise images, showed that the proposed methods acquired more matches and better stableness than WαSH. Experimentation on remote sensing images with less affine distortion and slight noise showed that the proposed methods obtained the correct matching rate greater than 90%. For images containing severe distortion, KAZE obtained a 35.71% correct matching rate, which is unacceptable for calculating the homography matrix, while IWWF achieved a 71.42% correct matching rate. IWWF was the only method that achieved the correct matching rate of no less than 50% for all four test stereo remote sensing image pairs and was the most stable compared to MSER, DWT-MSER, WαSH, DWT-WαSH, KAZE, WWF, and LIWWF.

scholarly journals A Fast and Efficient Adaptive Threshold Rate Control Scheme for Remote Sensing Images

2012 ◽  
Vol 2012 ◽  
pp. 1-6
Author(s):  
Xiao Chen ◽  
Xiaoqing Xu
Keyword(s):  
Remote Sensing ◽  
Rate Control ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Rate Distortion ◽  
Adaptive Threshold ◽  
Remote Sensing Images ◽  
Minimum Number ◽  
Code Complexity ◽  
Bit Planes

The JPEG2000 image compression standard is ideal for processing remote sensing images. However, its algorithm is complex and it requires large amounts of memory, making it difficult to adapt to the limited transmission and storage resources necessary for remote sensing images. In the present study, an improved rate control algorithm for remote sensing images is proposed. The required coded blocks are sorted downward according to their numbers of bit planes prior to entropy coding. An adaptive threshold computed from the combination of the minimum number of bit planes, along with the minimum rate-distortion slope and the compression ratio, is used to truncate passes of each code block during Tier-1 encoding. This routine avoids the encoding of all code passes and improves the coding efficiency. The simulation results show that the computational cost and working buffer memory size of the proposed algorithm reach only 18.13 and 7.81%, respectively, of the same parameters in the postcompression rate distortion algorithm, while the peak signal-to-noise ratio across the images remains almost the same. The proposed algorithm not only greatly reduces the code complexity and buffer requirements but also maintains the image quality.

Remote Sensing Image Enhancement Based on Wavelet Transformation

2012 ◽  
Vol 198-199 ◽  
pp. 223-226
Author(s):  
Ying Zhao ◽  
Ye Cai Guo
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Theoretical Analysis ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Original Image ◽  
Remote Sensing Images ◽  
Enhancement Algorithm

The contrast of remote sensing images is very low, which include various noises. In order to make full used of remote sensing image information extraction and processing, the original image should have to be enhanced. In this paper the enhancement algorithm based on the biothogonal wavelet transform is proposed. Firstly, we have to eliminate the beforehand noise, and then take advantage of the non-linear wavelet transform to enhanced low-frequency and high- frequency coefficient respectively. Finally, the new picture is reconstruct by the transformed low-frequency and high-frequency coefficient. The efficiency of the proposed algorithm was proved by the theoretical analysis and computer simulations.

JPEG2000 compression for remote sensing images with precise quality control

2013 ◽  
Vol 21 (8) ◽  
pp. 2180-2186
Author(s):  
宋蓓蓓 SONG Bei-bei ◽  
孙文方 SUN Wen-fang
Keyword(s):  
Remote Sensing ◽  
Quality Control ◽  
Remote Sensing Images ◽  
Jpeg2000 Compression

scholarly journals A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

2020 ◽  
Vol 12 (19) ◽  
pp. 3115 ◽  
Author(s):  
Liqiong Chen ◽  
Wenxuan Shi ◽  
Cien Fan ◽  
Lian Zou ◽  
Dexiang Deng
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
False Alarms ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Optical Remote Sensing ◽  
Dense Network ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Coarse To Fine

Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.

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