ABSTRACT This paper presents a novel triple‐color watermarking algorithm that leverages the elliptical monogenic wavelet transform (EMWT) and singular value decomposition (SVD) for robust and secure watermark embedding. The proposed method first encrypts the host image using a random matrix. Subsequently, both the host and watermark images undergo preprocessing techniques, including EMWT, discrete wavelet transform (DWT), discrete cosine transform (DCT), and SVD, to obtain the diagonal matrices required for embedding. For enhanced security, the watermark images are encrypted using the Arnold method with phase and linear representation embedding, where the second key is derived from the host image via Fourier transform. The watermarked image is then generated by applying the corresponding inverse transforms. Experimental results demonstrate the algorithm's excellent embedding and extraction performance on color images and icons with characters. Notably, the proposed method exhibits strong robustness against various attacks, including non‐geometric attacks (e.g., noise, filtering), geometric attacks (e.g., rotation, cropping), and mixed attacks, outperforming existing color watermarking methods in terms of anti‐attack ability and robustness.

Phragmites australis (common reed) is a typical one of wetland vegetation types, it is also considered as an aggressive invader vegetation in regional ecosystem, especially in North America. This paper is to explore the applicability and utility of estimating the aboveground biomass (AGB) of common reed in coastal wetlands using hyperspectral and LiDAR data, and evaluate the estimation errors caused by different vegetation types. Results showed that the AGB of common reed can be effectively estimated using a multivariable linear regression model based on highly-correlated vegetation features which were derived from hyperspectral and LiDAR data. The optimal AGB estimation model of common reed was developed by variables stepwise entry method with R2 of 0.845, RMSE of 0.19 kg/rrr', P of 7.63% and RPD of 2.92. The model accuracy of common reed was higher than that of wetland vegetation for mixed vegetation types. The estimation results indicated that the total AGB of common reed in the study area was up to 1866.86t, and pixels with biomass distributed between 1.82 kg/m? and 2.70 kg/m? were the most concentrated, and the spatial distribution of the estimated AGB of common reed was consistent with the field survey results.

In this paper, we performed a classification of the degree of imperviousness in Miyun urban areas of China's Beijing region based on object-oriented method with SPOT multi-spectral data. The approach mainly involved establishing an image object hierarchy consisting of three levels of different scale. With the integration of different object features and semantic information from different image object levels, we performed a final class-related classification in the middle level using the above super-scale and sub-scale information. After the classification completed, a thematic map of showing the degree of imperviousness in urban areas has been created. It showed that the object-oriented remote sensing image analysis method is an effective, simple and rapid way to estimate and map the degree of urban impervious surface.

Analysis of the degree of urban impervious surface can provide scientific information for urban-related issues. In this paper, we performed a classification of the degree of imperviousness in Miyun urban areas of China's Beijing region based on object-oriented method with Landsat TM data in 2006. The approach mainly involved establishing an image object hierarchy consisting of three levels of different resolution. The three levels were: (1) a classified super-scale image object level by fusing a pre-classified thematic image objects, (2) a classified sub-scale image object level by multi-resolution segmentation in a very high resolution, and (3) a middle level of image objects by multi-resolution segmentation in a relative middle scale. We performed a final class-related classification in the middle level using the above super-scale and sub-scale information. After the classification completed, a thematic map of showing the degree of imperviousness in urban areas has been created.

Abstract—In this paper, we developed a new building extraction system applied on high resolution remote sensing imagery based on multi-scale object oriented classification and probabilistic Hough transform. This can be divided into two different phases: building roof extraction, and shape reconfiguration. For the first phase, the multispectral and panchromatic high resolution satellite imageries are firstly fused for spatial resolution improvement and color information enhancement. The multi-resolution image segmentation is applied on the fused image, resulting in the formation of the different level of polygon primitives at different space scale, providing different view of the scene at different resolution. In addition to the spectral information, the tone, texture, shape, context information is evaluated in an object oriented manner. The classification is based on a fuzzy rule decision tree classifier. By fuzzy evaluating of the shape, texture, context and spectral information, building roofs are extracted by reconstruction and classification from an appropriate space scale of roof polygon primitives. For the shape reconfiguration phase, we adopt the probabilistic Hough transform to delineate the roof dominant line which shows the major orientation of the specific building roof. According to the dominant line, a building squaring algorithm is applied based on rectilinear fitting of the building boundary. It is shown by our experiment that most rectangular building roofs can be correctly detected, extracted and reconfigured, demonstrating the potential application of the method.

All coherent imaging techniques require extreme phase stability. Phase fluctuations can be induced by platform motion error or microwave propagation effect in turbulent atmosphere. Thus phase error correction can't be neglected for advanced precision airborne SAR imaging processing. This paper introduces an experiment of high-resolution airborne SAR imaging based on phase gradient autofocus. This method is developed to compensate the phase error in SAR signal while the aircraft platform motion compensation system is not provided. The whole algorithm can be divided into the following steps: SAR complex image creation; center cyclic shifting of the strongest point target; adding window for the point target; fast Fourier transform in the azimuth direction; estimation of phase error; phase error correction and inverse fast Fourier transform. Experiments show that the imaging algorithm based on phase gradient autofocus can effectively measure and compensate the phase error, thus obtain an obviously improved airborne SAR image in azimuth resolution, which is 2 meters in the experiment compared with 6 meters achieved by using conventional imaging method. The processing results proved the effectiveness of this algorithm for high-resolution airborne SAR imaging.

The Poyang Lake, as one of the most frequently flooded area, is the largest freshwater lake in China. It is very significant to monitor the water level and water regime accurately and in real-time during flooding. To provide an operational method to measure shape parameters, such as water level, water regime area, and water distribution of Poyang Lake using remote sensing images, we carried out the study. In the paper, using the hydrological statistical data of Poyang Lake through many years, we fit a mathematic model of water regime area varied with water level. Meanwhile, water regime distribution is extracted from Radarsat SAR and Terra Modis remote sensing images, and then its area can be calculated. Comparisons between model results and remote-sensing-image-based measuring results are carried out and the absolute error is less than 0.70%. Then we revise the fitted mathematic model using remotely sensed data and finally build a high precision (R/sup 2/>0.99) and practical mathematical model of water level versus water distribution of Poyang Lake main regime. It is a great contribution to the prevention, monitoring and mitigation of great flood disaster around the Poyang Lake district.

In order to make an accurate estimation of how much rice has been planted, it is essential to calculate the area of rice fields as precise as possible. Therefore, to delineate the edge of rice fields and remove the area occupied by the widened ditch roads around the rice fields caused by corner reflector effect in SAR images, have become a critical topic in rice monitoring and yield estimation using SAR images. To detect the linear features of rice fields for rice monitoring, This work introduces an edge detection and extraction scheme in Radarsat SAR images by applying a two-dimensional (2D) filter in eight directions. To reduce the isolated speckle noise on SAR images, an /spl sigma/-filter is performed firstly and then the two-dimensional filter in eight directions can be selected and applied to detect edges. Finally, a nonlinear operator is used to threshold its magnitude and produces the edge maps of Radarsat SAR images. The proposed scheme achieves positive results of extracting linear features, reducing the speckle noise effectively, and meeting the need of practical radar remote sensing application in rice monitoring and yield estimation.

Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator for desertification assessment. The primary purpose of this study was to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study were a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) was used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of sample data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over a 20-year period for which data were available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification. The result was validated by comparison of the theoretical results to land cover maps in different years. This experiment demonstrated the feasibility of applying the CoV regression methodology and long term NOAA-AVHRR NDVI time-series data for desertification monitoring in central Asia and western China.

This paper will investigate the effectiveness of the genetic algorithm evolved neural network classifier and its application on the land cover classification of multi-spectral remotely sensed imagery. First, the key issues of the algorithms and the procedures are described in detail. Second, SPOT XS imagery is employed to evaluate its accuracy. Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also incorporated for a comparison purpose. Based on an evaluation of the user's accuracy and kappa statistic of different classifiers, the superiority of applying the discussed genetic algorithm-based classifier for land cover classification using multi-spectral imagery data is established. Finally, some concluding remarks and suggestions are also presented.