Inspired by the block-sparsity theory, an extended block orthogonal matching pursuit algorithm by using the weighted operation and a new column-block selection strategy is proposed for tomography SAR imaging. By using neighboring pixels information in reconstruction, the proposed method can improve the performance of CS-based methods in the case of strong noise and a small number of baselines.
Experimental results confirm the effectiveness of the proposed method. Detecting and mapping Gonipterus scutellatus induced vegetation defoliation using WorldView-2 pan-sharpened image texture combinations and an artificial neural network. Therefore, the ability of remote sensing to detect and map G. In our study, an integrated approach using image texture in various processing combinations and an artificial neural network ANN were developed to detect and map G.
In order to improve the accuracy of detecting and mapping G. Using a sequential forward selection algorithm allowed for the selection of optimal texture combinations, which were subsequently input into a backpropagation ANN. Overall, our study highlights the potential of image texture combinations in improving the detection and mapping of vegetation defoliation.
Rule-based classification framework for remote sensing data. The land cover classification is an important task in geoscience applications. Many methods and implementations are based on multispectral data processing. The presented work aims to benefit from the nonlinear source separation process to enhance land cover identification. The source separation technique aims to provide underlying images and to compensate the mixing process.
Nonlinear separation is more realistic due to multiple distortions occurring on the radiance path from soil to sensors. The presented paper addresses pattern recognition for remote sensing and proposes a framework based on feature extraction and decisional fusion. The first stage performs a nonlinear separation model based on Bayesian inferences.
Nonlinearity is approximated by a multilayer neuron network. The separation process updates knowledge about unknown sources and model parameters iteratively.
The second stage performs feature extraction. Based on a decisional fusion, the third stage realizes a classification process. Second, a majority vote determines the final label. Experimentation results demonstrate that the proposed fusion method enhances the recognition accuracy and represents a powerful tool for land identification. Panchromatic image denoising by a log-normal-distribution-based anisotropic diffusion model.
Jagalingam Pushparaj , Muthukumaran Malarvel. An anisotropic diffusion model based on a log-normal distribution of a local gray-level is used to propose a way to denoise the panchromatic images. The implication of the low-edge gradient of the feature space for denoising and smoothing the noisy image is adaptively adjusted by the adaptive threshold parameter in a diffusion coefficient function. Furthermore, to terminate the diffusion process, an entropy-based stopping criterion is implemented. In order to analyze the performance of the models, quantitative metrics such as standard deviation, entropy, and the signal-to-noise ratio of a two-dimensional line profile are used.
For further analysis, the results of denoising models are segmented using entropy-based segmentation techniques such as Harvda, Renyi, Kapur, and Yen models. A misclassification error metric is used to evaluate the segmentation results. The metric results show that the proposed model effectively removes the noise and preserves the features of a panchromatic image.
Vegetation water content VWC is an important land surface parameter that is used in retrieving surface soil moisture from microwave satellite platforms.
Operational approaches utilize relationships between VWC and satellite vegetation indices for broad categories of vegetation, i. Determining crop type—specific equations for water content could lead to improvements in the soil moisture retrievals. Both sites are monitored for soil moisture, and the calibration and validation assessments had indicated performance issues in both domains. One possible source could be the characterization of the vegetation. In this investigation, Landsat 8 data are used to compute a normalized difference water index for the entire summer of that is then integrated with extensive VWC sampling to determine how to best characterize daily estimates of VWC for improved algorithm implementation.
Additional crop-specific equations are developed for winter wheat RMSE of 0. Overall, the conditions are judged to be typical with the exception of soybeans, which had an exceptionally high biomass as a result of significant rainfall as compared to previous studies in this region. Future implementation of these equations into algorithm development for satellite and airborne radiative transfer modeling will improve the overall performance in agricultural domains.
Analysis of Landsat-derived multitemporal vegetation cover to understand drivers of oasis agroecosystems change. Oasis agroecosystems monitoring plays a significant role in the economic development, sustainable management, and policy-making of arid and Saharan regions. The aims of this study are to analyze the spatiotemporal changes of oasis vegetation and discuss possible driving forces of changes. This analysis employed field and ancillary data, geographic information system, Landsat imagery, and remote-sensing techniques. Minimum noise fraction is used for endmembers extraction, and spectral mixture analysis is applied to each image to extract vegetation fraction, which is used as an indicator of change.
Change detection is performed in six oases in south-eastern Morocco over eight separate periods from to using Landsat data. The pattern of the spatiotemporal changes in vegetation cover is analyzed using time- and space-oriented change detection algorithms. The results show that spectral mixture analysis yields high accuracies for oasis vegetation extraction in arid areas and accounts for mixed pixel issues. The results are discussed considering also climate and socioeconomic factors, showing that the driving forces of these dynamics are primarily anthropogenic.
Estimating the parameters of the generalized KA distribution by applying the expectation maximization algorithm. Generalization of the KA distribution is formulated by combining the class A and K distributions; the resulting distribution is termed as generalized KA distribution. It is obtained as a mixture of a generalized Rayleigh and a class A distribution with gamma-distributed mean intensity, and it may be used to describe clutter statistics.
Its parameters are estimated by implementing the expectation maximization algorithm. Sparse and low-rank matrix decomposition-based method for hyperspectral anomaly detection. A sparse and low-rank matrix decomposition-based method is proposed for anomaly detection in hyperspectral data. High-dimensional data are decomposed into low-rank and sparse matrices representing background and anomalies, respectively. The problem of the decomposition process is defined from the dictionary learning point of view. Therefore, our way of obtaining these matrices differs from previous studies.
It aims to find a correct partition of the data and separate anomaly pixels from the background. After decomposition, Mahalanobis distance is applied to the sparse part of the data to get anomaly locations. Three hyperspectral data sets are used for evaluation. Experimental results suggest that anomaly detection performance of the proposed method surpasses those of the state-of-the-art methods. Adaptive compensation for wideband radar system distortion based on cross range profiles. Inverse synthetic aperture radar is driven to a larger bandwidth to achieve a finer resolution.
However, severe amplitude-phase distortion of radar greatly destroys the amplitude and phase of the chirp signal and hence becomes a major challenge to the fulfillment of the desired high-resolution performance. An adaptive framework is proposed to compensate for the distortion of a wideband radar system without the measurement of its intrinsic characteristic.
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The distorted image of the target is obtained first and then used to get cross-range profiles CRPs. Based on the CRPs, amplitude distortion and phase distortion are compensated by the noise level and the autofocus algorithm, respectively. The proposed framework facilitates the compensation and works well in practical applications.
Performance of the proposed framework is validated on simulated and measured data. Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery. Land-cover classification using remote sensing imagery is an important part of environmental research because it provides baseline information for ecological vulnerability and risk assessment, disaster management, landscape conservation, local and regional planning, and so on. Rural-land-cover classification is challenging for both object-based image analysis methods and classifiers.
The objective of this study is to improve the object-oriented classification accuracy of rural land cover by combining two models based on high spatial resolution imagery. We apply the C5. The effectiveness of the model combination is assessed by comparing the classification results with the state-of-the-art machine learning algorithm, namely extreme gradient boosting XGBoost. The comparisons are done based on the classification results of both the study area and the case area. In terms of training time, XGBoost runs the slowest in the classifications of both the study area and the case area.
Rainfall retrieval and drought monitoring skill of satellite rainfall estimates in the Ethiopian Rift Valley Lakes Basin. Satellite-based rainfall products are essential for retrieving rainfall, particularly in data-scarce and drought-prone countries like Ethiopia. However, their quality needs to be validated prior to their use.
Therefore, we evaluated the performance of the Climate Hazards group Infrared Precipitation with Stations version 2. Their skill of retrieval was evaluated against ground-measured rainfall at dekadal, monthly, and seasonal scales across agroclimatic zones over to Generally, except for its slightly larger dekadal false alarm ratio, CHIRPS has achieved the highest and most consistent agreement with the reference data at all the timescales and agroclimatic classes. Consequently, CHIRPS was further assessed for its suitability of drought monitoring, and it has exhibited promising skill in detecting specific historical drought events.
Therefore, to overcome the scarcity of ground-measured rainfall data in the study area, we recommend the CHIRPS rainfall estimate to be used as an alternative data source for drought monitoring. Small unmanned aerial model accuracy for photogrammetrical fluvial bathymetric survey. Neil Entwistle , George Heritage. Fluvial systems offer a challenging and varied environment for topographic survey, displaying a rapidly varying morphology, vegetation assemblage, and degree of submergence.
Traditionally, theodolite- or global positioning satellite-based systems have been used to capture cross-section and breakline-based topographic data, which have subsequently been interpolated. The rise of structure from motion SfM photogrammetry, coupled with small, unmanned aerial vehicles sUAV , has the potential to record elevation data at reach scale subdecimeter density. The approach has the additional advantage over Lidar of seeing through clear water to capture bed details and also generating orthorectified photographic mosaics of the survey reach.
However, data accuracy has yet to be comprehensively assessed. Comparative analysis between theodolite survey and SfM suggests similar accuracy and precision across terrestrial surfaces with error lowest over solid surfaces, increasing with vegetation complexity. Significantly, associated error when linked to channel D 50 highlights the ability of unmanned aerial vehicles to capture accurate fluvial data across a range of river biotopes and depths to 2.
Three-dimensional modeling of alteration information with hyperspectral core imaging and application to uranium exploration in the Heyuanbei uranium deposit, Xiangshan, Jiangxi, China. Hyperspectral technology is particularly good at identifying hydrothermally altered minerals and can offer support for three-dimensional 3-D alteration modeling of hydrothermal deposit. We propose a 3-D alteration modeling method with hyperspectral core imaging to deepen hydrothermal alteration research for uranium exploration of the Heyuanbei uranium deposit in the western Xiangshan ore field, Jiangxi, South China.
Hyperspectral core imaging data from 14 drill cores from the Heyuanbei uranium deposit are used to establish a 3-D alteration model of the superimposed stratigraphy and structural information by this method. The modeling results indicate that large-scale illite and kaolinite—dickite alterations have developed in the Heyuanbei deposit.
The zoning characteristics reveal that the hydrothermal fluid is acidic in the upper cores, whereas in the lower cores, alkaline fluid is present, and the fluid environment has the characteristics of a transition from early alkaline to late acidic conditions. Moreover, the short-wavelength illite is more closely related to fluorite-hydromica-type uranium mineralization than the long-wavelength illite, and the conversion action from long-wavelength illite to short-wavelength illite is favorable for uranium enrichment.
Thus, short-wavelength illite, kaolinite, dickite, and fluorite can be used as prospective indicators for uranium exploration in the Heyuanbei mining area and nearby areas. Surface roughness is an important characteristic in analyzing synthetic aperture radar images and three-dimensional 3-D surfaces that influence the backscattering of electromagnetic waves. We propose an algorithm based on a fractal method for retrieving parameters of a surface. To estimate surface roughness parameters, 10, different 3-D surfaces with different rms-height and correlation length are simulated.
The results show that the fractal method represents a good relationship between roughness parameters and fractal dimension. It can be seen that, in some cases, surfaces with different roughness have the same fractal dimension. Roughness index RI can be used as a complement to fractal dimension. We present empirical relationships among fractal dimension, roughness parameters, and RI. The comparison between field measurement and estimated roughness showed that the accuracy of soil surface roughness estimation for band L with HH polarizations is better than bands L and C with polarizations HV and HH, respectively.
The results of this method also are compared with the roughness estimates using the integral equation model IEM. The analysis of outputs shows that the roughness estimation using the fractal and IEM is very similar in the low moisture at L band in HH polarization. The root mean square error of roughness for data at L band in HH polarization on July 1, , is 0. Hyperspectral band selection using structural information via hierarchical clustering. Ibrahim Delibasoglu , Mufit Cetin. The techniques of unsupervised band selection are important in the processing of hyperspectral images, such as dimension reduction and abstraction of meaningful information.
These techniques aim to select the most informative bands from the original dataset using some similarity metrics and search strategies. For this purpose, the structural information of the images can be utilized in many applications, such as the compact representation and classification of hyperspectral images. Hierarchical clustering method was chosen as the searching strategy and interest points, such as clear edges, blobs, and boundaries of objects, were used as the structural information during the selection of representative bands.
At the same time, a similarity criterion was developed to generate a representative band subset from the hyperspectral bands. Finally, the representative band subsets obtained from the proposed methods were classified by the k-nearest neighborhood algorithm and the classification accuracy results were taken as performance criteria.
The performances of the proposed methods were compared with the Walumi and Waludi techniques that are common in the literature. Experimental results show that the proposed methods give better results than the others. Algorithms for the classification and characterization of aerosols: utility verification of near-UV satellite observations. Aerosol types were characterized and classified using multispectral satellite data. An absorbing aerosol index AAI was proposed and defined as the ratio of the satellite-observed radiance R at a wavelength of 0.
Not only the AAI index but also the short-wavelength infrared measurements were utilized to determine the dust detection index DDI defined as the ratio of R 2. An understanding of aerosol types facilitated subsequent aerosol retrieval. The proposed algorithms are expected to be available not only for the analysis of the SGLI data but also for other future missions. There are different algorithms to estimate the energy balance equation. This equation expresses net radiation converting to other forms of radiation fluxes. The computation of the equation is helpful to consider energy exchange between the atmosphere and the surface.
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Therefore, testing different methods could help us in finding a more accurate way to estimate the energy balance equation. The simple algorithm for evapotranspiration retrieving SAFER algorithm is a method to estimate energy balance equation parameters. Mashhad area in northeast of Iran located in a semiarid region was chosen as the study area. The results of method 1 and method 2 were compared.
Albedo and surface temperature values that were spatially distributed in method 1 lead to a wide variation range in the calculated energy terms. However, these parameters, which were considered as constant values, made energy terms have limited variation range in method 2. The differences in the values were insignificant in the large-scale calculation, which can be ignored.
This algorithm can be used for energy balance equation estimation in Mashhad. Extended wavenumber domain algorithm for equivalent squinted linear frequency modulated continuous wave SAR imaging. Ability to acquire high-resolution, phase-preserved images is an important requirement in modern synthetic aperture radar SAR applications. Wavenumber domain algorithms are good choices to achieve this objective without approximation in the wavenumber reversal process. However, when squint angle gets larger, even wavenumber domain algorithms suffer from resolution and computational efficiency degradations.
In order to accommodate wavenumber domain algorithms to highly squinted SAR imaging by exploiting squint minimization and two-dimensional interpolation schemes, the proposed extended wavenumber domain algorithm possesses the advantages of maximum spectrum utilization, processing efficiency, and good interpretation of the two-dimensional interpolation.
It can also be easily incorporated with motion compensation without complicating the motion analysis. Simulations and real data experiments validate our proposed algorithm. Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: operational method and its implementation for a mountainous area of Nepal. In the context of land-use and land-cover LULC classification, there is a lack of leverage of the recent increase of the ease of access to satellite imagery data, cloud computing platforms, and classification techniques.
We present both the development of an operational method for LULC classification that considers these progresses and the implementation of this operational method for a mountainous area of Nepal. The operational method can help the producers of LULC maps conduct future work on areas in developing countries, as such contributing to addressing various issues that involve land use. Scenes are segmented with a mean-shift segmentation algorithm. Objects are classified with an expectation maximization algorithm into clusters with different temporal signatures and are assigned to six information classes: water, bulrush marshes, short broad-leaf marshes, tall broad-leaf marshes, short grasslands and grass marshes, and tall grasslands and grass marshes.
The obtained product has a global accuracy of We point out the usefulness of X-band for flood monitoring and macrophyte vegetation type discrimination. However, we find limitations for the discrimination between high-biomass vegetation targets, such as tall broad-leaf marshes and tall grasslands. In a mosaic of herbaceous wetlands, the knowledge on the relation between vegetation and floods is essential for interpreting and predicting how backscattering coefficients and other synthetic aperture radar-derivated parameters vary with flooding. Dimensionality reduction based on parallel factor analysis model and independent component analysis method.
Independent component analysis ICA reduces the spectral dimension and does not utilize the spatial information of the HSI. The experimental results indicate that this method improves the classification compared with the previous methods. Image and Signal Processing Methods. Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields.
Aerial images acquired by multiple sensors provide comprehensive and diverse information of materials and objects within a surveyed area. The current use of pretrained deep convolutional neural networks DCNNs is usually constrained to three-band images i. Additional spectral bands from a multiple sensor setup introduce challenges for the use of DCNN. We fuse the RGB feature information obtained from a deep learning framework with light detection and ranging LiDAR features to obtain semantic labeling. Specifically, we propose a decision-level multisensor fusion technique for semantic labeling of the very-high-resolution optical imagery and LiDAR data.
Our approach first obtains initial probabilistic predictions from two different sources: one from a pretrained neural network fine-tuned on a three-band optical image, and another from a probabilistic classifier trained on LiDAR data. From space or aircraft land survey radars with synthetic aperture antenna obtained data passes through several stages of processing.
The most responsible, time-consuming and difficult process is one of decrypting are radar images. This process refers to the intellectual and hard-formalized type of human activity and exactly it is key in determining the efficiency of the whole system remote sensing as a whole. As long as research on the development of the theory and practice of radar images decryption in the current development of remote sensing surface is an important direction in the development of science-based foundations of maintenance the radar means of remote sensing surface.
Artiushyn L. B , Aerospace intelligence in local wars of our time. Ris U. Kaufman, A. Kuzmicheva, Moscow, Tehnosfera, p. Radar digital synthetic aperture antenna, , [Radiolokatsionnyie stantsii s tsifrovyim sintezirovaniem aperturyi antennyi], V. Antipov, V. Goryainov, A. Kulin i dr. Alyabev A. Alyabev, V. Kobernichenko, Geodeziya i kartografiya, No 5, pp. Verba V. Shkolnyiy L. Shkolnogo L.
Zhukovskogo, p. Two different test areas were defined to make the quality assessments as urban and agricultural areas. Each of the selected areas covers 11 km 2 approximately. It has been observed that some of the methods have enhanced either spatial quality or preserved spectral quality of the original SPOT XS image to various degrees while some approaches have introduced distortions. We will discuss these in detail in the upcoming sections. To avoid the effects of opposite passes, both SAR images were chosen in ascending orbits. Before the image fusion process SAR images were pre-processed by the commonly used speckle reducing filter techniques.
For the filtering of SAR images, among the different sized kernel windows, Gamma filtering of 3x3 kernel size was chosen to suppress the speckle noise. In this study image fusion was conducted at the pixel level. In order to avoid the combination of unrelated data, spatial registration accuracies should be at the sub pixel level. Particularly in SAR data terrain distortions are mainly the causes of the combination of unrelated pixels during the fusion processes. To remove the possible terrain distortions see Section 2 , it is essential to register images perfectly.
These geometric distortions are different from optical distortions, and they may be severe in rough topographic areas. If the creation of co-registered datasets is not accurate, the quality of the fused image will decrease significantly. Therefore in fusion applications geometric correction is very important for the registration of the images. Resulting fused images were resampled to the higher resolution of SAR images as 8m x 8m.
Short explanations of the approaches used for fusion are given below:. Highpass Filtering uses a band addition approach to fuse both spectral and spatial information of the images. For this purpose, a high resolution image is filtered with a high pass filter to compute the high frequency component.
High frequency component, which is concerned to spatial information, is added pixel by pixel basis to each low resolution multispectral images WANG et al. In conclusion, by adding a filter to a low resolution band, spatial information content of the high resolution image is replaced and seen in the fused image BETHUNE et al. Principal Component Analysis converts a multivariate data set of inter-correlated variables into new uncorrelated linear combinations of the original values.
The principal component domain of the multispectral image is created by principle component transformation. It reduces the dimensionality of the data set due to having high correlation between multispectral bands. The first PC contains more information since it has a large percentage of all the variance. Intensity Hue Saturation method transforms a low resolution 3-band image as red R , green G , blue B to intensity I , hue H , and S saturation components where I refers to the total brightness of the image, H to the dominant or average wavelength of the light contributing to the colour, and S to the purity of the colour EHLERS et al.
Next the intensity component, which is the spatial information of the image, is replaced with a high resolution image to enhance the spatial resolution. The disadvantage of IHS is that, it can only process three bands of a multispectral image. In Discrete Wavelet Transformation , a high resolution image is separated in to its low and high frequency components.
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The aim is to select the base of a waveform to be used. Once the basis waveform is mathematically defined, a family of multiples can be created with increasing frequency as retaining the high-pass images for later image reconstruction. In practice, three or four recursions are sufficient i. Here the high resolution image is decomposed to its low and high frequencies.
Multi spectral image is replaced with the low pass image which has same resolution. In Ehlers Fusion , first three low resolution multispectral band images are transformed to an IHS image. Later a two dimensional Fast Fourier Transformation FFT is used to transform the intensity I component of the image and a high resolution image into the frequency domain. Than a low pass filter is applied on the intensity spectrum, and for the spectrum of high resolution image an inverse high pass filter is used.
An inverse FFT is performed on these filtered images, and a new fused intensity image component is formed by adding these filtered images together. New intensity is composed with the high and low frequency information that are extracted from high and low resolution images respectively. In image fusion processes quality refers to both spectral and spatial quality of fused images. The aim of image fusion techniques is to inject the spatial detail into the multispectral MS imagery while keeping the original spectral values.
Today although used standard image fusion methods are often successful, the spectral truth remains in the merged images to be checked quantitatively in order to evaluate the performance of each applied fusion algorithm precisely EHLERS et al. In general, reference MS images at higher spatial resolution with the same spectral intervals of input MS images are not available for assessing the quality of the fused images. The lack of availability of these reference images makes quality assessment particularly difficult WALD et.
The only available reference images are the original MS images at the "low" spatial resolution. To overcome this problem there are two ways. It is either degrading the fused image back to the original image resolution prior to assessment or degradation of both pan and multispectral imagery by the same factor prior to fusion. In this way a number of statistical criteria can be calculated to verify the accuracy of fused images. As a result fused images were visually and statistically evaluated for colour preservation, spatial enhancement and for spectral fidelity respectively.
To evaluate the preserved spectral quality of the each image fusion techniques used, the original SPOT XS image was compared with 10 resulting fused images in terms of information improvement and fidelity of spectral characteristics. The comparison was performed by statistical and graphical interpretation Table 2 , Table 3 , and Figure 5 , Figure 6. Prior to the statistical comparisons, the fused images were downsampled to the spatial resolution of the reference image i. Visual evaluations Figure 3 , Figure 4 and statistical analysis were performed considering the local characteristics as urban and agricultural areas rather than performing globally on the entire image scenes of the fused data.
For the graphical analyses, a transect Figure 7 was defined on the same section of the images including urban and agricultural areas Figure 8 and Figure 9. Quality of the spatial resolution was analysed comparing the features like field borders, roads and buildings visually. It is depicted that all methods enhance spatially SPOT-XS image to various degrees but some methods also introduce spectral distortions.
On the other hand, comparing the spatial quality of all the fused images Figure 3 and 4 visually, it is obvious that the spatial characteristics inherited from SAR images are more apparent in PCA and HPF given in this order.
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DWT follows them in the third order. It produces significant colour distortion with respect to the original SPOT XS image, and among all it is the worse in preserving colours. Meanwhile, as seen from the output images, Ehlers method keeps spectral consistency better than other methods. Particularly the colours of the resulting Ehlers images of agricultural and urban areas are almost the same as that of the original SPOT XS image.
For both urban and rural test sides; IHS method shows border lines and roads much sharper in agricultural areas whereas it is the worse in residential areas with its noisy texture. In radar images, ground penetration depends on the wavelength. It increases with longer wavelengths. Ground penetration is inversely depends on the complex di-electric constant which means that the higher the water content on the ground surface, the higher the reflectivity of radar waves.
Due to the dense features of urban areas, it easier to see the properties of the bands in the agricultural areas than those of in the urban areas. Especially in bare lands the backscatter values are differing according to the penetration in C band and L band SAR data. In agricultural areas better results were obtained with L band images especially for different reflectivity content in the fields and for extracting border lines of the fields and roads. Here we provide assessments specifically for urban and agricultural areas shown in Figures 3 and 4.
However, best performance on the values of correlations was obtained from Ehlers' method in this study. This study also revealed that HPF method performs better in the agricultural areas.