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ECG signals tagged with secret information are transferred through wireless communication channel in remote health monitoring applications. To hide secret information, the proposed steganography system uses ECG signal as cover data. The watermarked data (grey scale image or ECG signal) is transformed into 2D binary matrix (QR code), to enhance security of the steganography process. The Base64 encoding technique converts unsigned integer values to alphanumeric cypher text, which is then turned into a 2D binary matrix (QR code) through a QR code generator/reader. The threshold selection algorithm is used to select the coefficient position, and the pixel swapping technique is employed to incorporate watermark data into the selected location. Signal degradation is minimized by selecting coefficient locations are near to zero. The imperceptibility of the watermarked ECG signal is evaluated using performance metrics such as Peak Signal to Noise Ratio (PSNR), Percentage Residual Difference (PRD), Correlation Coefficient (CC), and Structural Similarity Measure Index (SSIM). Bit Error Rate is another metric used to evaluate the quality of extracted watermark data (BER). The watermarked signals imperceptibility is found to be good and is within the ideal value. Increase in payload capacity has increased signal deterioration. The steganography scheme has no BER, and the reconstructed signal is identical to the cover ECG signal. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH), a promising data-hiding technique, is widely examined in domains such as medical image transmission, satellite image transmission, crime investigation, cloud computing, etc. None of the existing RDH schemes addresses a solution from a real-time aspect. A good compromise between the information embedding rate and computational time makes the scheme suitable for real-time applications. As a solution, we propose a novel RDH scheme that recovers the original image by retaining its quality and extracting the hidden data. Here, the cover image gets encrypted using a stream cipher and is partitioned into non-overlapping blocks. Secret information is inserted into the encrypted blocks of the cover image via a controlled local pixel-swapping approach to achieve a comparatively good payload. The new scheme M P S A allows the data hider to hide two bits in every encrypted block. The existing reversible data-hiding schemes modify the encrypted image pixels leading to a compromise in image security. However, the proposed work complements the support of encrypted image security by maintaining the same entropy of the encrypted image in spite of hiding the data. Experimental results illustrate the competency of the proposed work accounting for various parameters, including embedding rate and computational time. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH), a promising data-hiding technique, is widely examined in domains such as medical image transmission, satellite image transmission, crime investigation, cloud computing, etc. None of the existing RDH schemes addresses a solution from a real-time aspect. A good compromise between the information embedding rate and computational time makes the scheme suitable for real-time applications. As a solution, we propose a novel RDH scheme that recovers the original image by retaining its quality and extracting the hidden data. Here, the cover image gets encrypted using a stream cipher and is partitioned into non-overlapping blocks. Secret information is inserted into the encrypted blocks of the cover image via a controlled local pixel-swapping approach to achieve a comparatively good payload. The new scheme MPSA allows the data hider to hide two bits in every encrypted block. The existing reversible data-hiding schemes modify the encrypted image pixels leading to a compromise in image security. However, the proposed work complements the support of encrypted image security by maintaining the same entropy of the encrypted image in spite of hiding the data. Experimental results illustrate the competency of the proposed work accounting for various parameters, including embedding rate and computational time.

The classification of hyperspectral data by means of per pixel classifiers forces each mixed pixel to map onto a single class, whereas sub-pixel classifiers are incapable of spatial arrangement of the land cover classes at its sub-pixel level. Super resolution mapping technique takes advantage of fractional abundance of each pixel and its surrounding pixels to make the classified image much finer spatial resolution. The pixel to be super resolved (PTS) is divided into equal number of rows and columns, according to pre-defined zoom factor. The spatial proximity of the pixel is also considered in mapping at the sub-pixel level of the hyperspectral data. Now, each sub-pixel of the PTS is modelled as linear combination of number of sub-pixels allotted to the neighbouring pixels with pre-defined weights, and here they are 8 and 17, which directly depend upon the spatial location or proximity of the sub-pixels of PTS and neighbouring pixels. Irrespective of the sizes of the classes, all classes are treated equal while filling the sub-pixels in that PTS, which preserves small classes or targets of the image. Experiments have been carried out on a synthetic data and two hyperspectral datasets of different nature. The overall accuracy of super resolution mapping for synthetic data comes to be 96.3% for the whole image, while the accuracy for super resolved of only mixed pixels comes to be 86.3%. Further, experiments on real hyperspectral datasets have been carried out, and the overall accuracy comes to be more than 95% for both the datasets. [ABSTRACT FROM AUTHOR]

Boundary pixels of rivers are subject to a spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super-resolution mapping (SRM) are conducted in two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for the normalized difference water index (OBA-NDWI) is proposed for identifying the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) algorithm and interpolation-based algorithms are also applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2 = 0.9, RMSE = 7% for eight-bandWorldView-3 (WV-3) image and R2 = 0.87, RMSE = 9% for GeoEye image). The spectral bands of WV-3 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas by about 10% with respect to conventional hard classification. [ABSTRACT FROM AUTHOR]

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43-10.96% higher than that of the IGSRM method for different scale factors, and 1.09-3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42-4.92%, and 0.08-0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods. [ABSTRACT FROM AUTHOR]

Boundary pixels of rivers are subject to a spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super-resolution mapping (SRM) are conducted in two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for the normalized difference water index (OBA-NDWI) is proposed for identifying the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) algorithm and interpolation-based algorithms are also applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2 = 0.9, RMSE = 7% for eight-band WorldView-3 (WV-3) image and R2 = 0.87, RMSE = 9% for GeoEye image). The spectral bands of WV-3 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas by about 10% with respect to conventional hard classification.

The aim of this letter is to present a spatio–temporal pixel-swapping algorithm (STPSA), based on conventional pixel-swapping algorithms (PSAs), in which both spatial and temporal contextual information from previous land cover maps or observed samples are well integrated and utilized to improve subpixel mapping accuracy. Unlike conventional pixel-swapping algorithms, STPSA is capable of utilizing prior information, which was previously ignored, to predict the attractiveness based on pairs of subpixels. This algorithm involves three main steps and operates in an iterative manner: 1) it predicts the maximum and minimum attractiveness of each pair of pixels; 2) ranks the swapping scores based on the attractiveness of all the pairs; and 3) swaps the locations of the pair of pixels with a maximum score to increase the objective function. Experiments with actual satellite images have demonstrated that the proposed algorithm performs better than other algorithms. In comparison, the proposed STPSA's better performance is due to the fact that prior information used in other algorithms is restricted to a percentage level rather than the real subpixel level. [ABSTRACT FROM PUBLISHER]

In this paper, a block based steganographic algorithm has been proposed where a sequence of secret bits are embedded into a set of pixels by rearranging the pixel locations. This algorithm has been devised as an improvement over existing statistical restoration based algorithms in order to reduce the additive noise which occurs due to embedding. It is shown that the proposed scheme substantially reduces the additive noise compared to existing statistical restoration based schemes. [ABSTRACT FROM AUTHOR]

Combining super-resolution techniques can increase the accuracy with which the shape of objects may be characterised from imagery. This is illustrated with two approaches to combining the contouring and pixel swapping methods of super-resolution mapping for binary classification applications. In both approaches, the output of the pixel swapping method is softened to allow a contour of equal class membership to be fitted to it to represent the inter-class boundary. The accuracy of super-resolution mapping with the individual and combined techniques is explored, including an assessment of the effect of variation in the number of neighbors and zoom factor on pixel swapping based analyses. When combined, the error with which objects of varying shape were represented was typically greatly reduced relative to that observed from the application of the methods individually. For example, the root mean square error in mapping the boundary of an aeroplane represented in relatively fine spatial resolution imagery decreased from 14.41 m with contouring and 4.35 m with pixel swapping to 3.07 m when the approaches were combined. [ABSTRACT FROM PUBLISHER]