Showing papers in "IEEE Geoscience and Remote Sensing Letters in 2014"
TL;DR: This letter proposes SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval, and results show slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.
Abstract: The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 $\mu\hbox{m}$ , thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.
607 citations
TL;DR: Comparative experimental results indicate that the proposed HDNN significantly outperforms the traditional DNN on vehicle detection, by dividing the maps of the last convolutional layer and the max-pooling layer of DNN into multiple blocks of variable receptive field sizes or max- pooling field sizes to enable the HDNN to extract variable-scale features.
Abstract: Detecting small objects such as vehicles in satellite images is a difficult problem. Many features (such as histogram of oriented gradient, local binary pattern, scale-invariant featuretransform, etc.) have been used to improve the performance of object detection, but mostly in simple environments such as those on roads. Kembhavi et al. proposed that no satisfactory accuracy has been achieved in complex environments such as the City of San Francisco. Deep convolutional neural networks (DNNs) can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DNN has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. In this letter, we present a hybrid DNN (HDNN), by dividing the maps of the last convolutional layer and the max-pooling layer of DNN into multiple blocks of variable receptive field sizes or max-pooling field sizes, to enable the HDNN to extract variable-scale features. Comparative experimental results indicate that our proposed HDNN significantly outperforms the traditional DNN on vehicle detection.
583 citations
TL;DR: A robust IR small target detection algorithm based on HVS is proposed to pursue good performance in detection rate, false alarm rate, and speed simultaneously.
Abstract: Robust human visual system (HVS) properties can effectively improve the infrared (IR) small target detection capabilities, such as detection rate, false alarm rate, speed, etc. However, current algorithms based on HVS usually improve one or two of the aforementioned detection capabilities while sacrificing the others. In this letter, a robust IR small target detection algorithm based on HVS is proposed to pursue good performance in detection rate, false alarm rate, and speed simultaneously. First, an HVS size-adaptation process is used, and the IR image after preprocessing is divided into subblocks to improve detection speed. Then, based on HVS contrast mechanism, the improved local contrast measure, which can improve detection rate and reduce false alarm rate, is proposed to calculate the saliency map, and a threshold operation along with a rapid traversal mechanism based on HVS attention shift mechanism is used to get the target subblocks quickly. Experimental results show the proposed algorithm has good robustness and efficiency for real IR small target detection applications.
324 citations
TL;DR: In this letter, two different injection methodologies are compared and the superiority of contrast-based methods both by physical consideration and by numerical tests carried out on remotely sensed data acquired by IKONOS and Quickbird sensors are motivated.
Abstract: The pansharpening process has the purpose of building a high-resolution multispectral image by fusing low spatial resolution multispectral and high-resolution panchromatic observations. A very credited method to pursue this goal relies upon the injection of details extracted from the panchromatic image into an upsampled version of the low-resolution multispectral image. In this letter, we compare two different injection methodologies and motivate the superiority of contrast-based methods both by physical consideration and by numerical tests carried out on remotely sensed data acquired by IKONOS and Quickbird sensors.
302 citations
TL;DR: The method uses total variation to regularize an ill-posed problem dictated by a widely used explicit image formation model and produces images of excellent spatial and spectral quality.
Abstract: In this letter, we present a new method for the pansharpening of multispectral satellite imagery. Pansharpening is the process of synthesizing a high spatial resolution multispectral image from a low spatial resolution multispectral image and a high-resolution panchromatic (PAN) image. The method uses total variation to regularize an ill-posed problem dictated by a widely used explicit image formation model. This model is based on the assumptions that a linear combination of the bands of the pansharpened image gives the PAN image and that a decimation of the pansharpened image gives the original multispectral image. Experimental results are based on two real datasets and the quantitative quality of the pansharpened images is evaluated using a number of spatial and spectral metrics, some of which have been recently proposed and do not need a reference image. The proposed method compares favorably to other well-known methods for pansharpening and produces images of excellent spatial and spectral quality.
242 citations
TL;DR: Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random Forest, indicating that R rotation Forests are promising approaches for generating classifier ensemble of hyperspectral remote sensing.
Abstract: In this letter, an ensemble learning approach, Rotation Forest, has been applied to hyperspectral remote sensing image classification for the first time. The framework of Rotation Forest is to project the original data into a new feature space using transformation methods for each base classifier (decision tree), then the base classifier can train in different new spaces for the purpose of encouraging both individual accuracy and diversity within the ensemble simultaneously. Principal component analysis (PCA), maximum noise fraction, independent component analysis, and local Fisher discriminant analysis are introduced as feature transformation algorithms in the original Rotation Forest. The performance of Rotation Forest was evaluated based on several criteria: different data sets, sensitivity to the number of training samples, ensemble size and the number of features in a subset. Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random Forest. They indicate that Rotation Forests are promising approaches for generating classifier ensemble of hyperspectral remote sensing.
194 citations
TL;DR: This paper presents an approach to image time series analysis which is able to deal with irregularly sampled series and which also allows the comparison of pairs of time series where each element of the pair has a different number of samples.
Abstract: Earth observation satellites are now providing images with short revisit cycle and high spatial resolution. The amount of produced data requires new methods that will give a sound temporal analysis while being computationally efficient. Dynamic time warping has proved to be a very sound measure to capture similarities in radiometric evolutions. In this letter, we show that its nonlinear distortion behavior is compatible with the use of a spatiotemporal segmentation of the data cube that is formed by a satellite image time series (SITS). While dealing with spatial and temporal dimensions of SITS at the same time had already proven to be very challenging, this letter proves that, by taking advantage of the spatial and temporal connectivities, both the performance and the quality of the analysis can be improved. Our method is assessed on a SITS of 46 Formosat -2 images sensed in 2006, with an average cloud cover of one third. We show that our approach induces the following: 1) sharply reduced memory usage; 2) improved classification results; and 3) shorter running time.
179 citations
TL;DR: A fast nonlocal despeckling filter that combines a variable-size search area driven by the activity level of each patch, and a probabilistic early termination approach that exploits speckle statistics in order to speed up block matching.
Abstract: Despeckling techniques based on the nonlocal approach provide an excellent performance, but exhibit also a remarkable complexity, unsuited to time-critical applications. In this letter, we propose a fast nonlocal despeckling filter. Starting from the recent SAR-BM3D algorithm, we propose to use a variable-size search area driven by the activity level of each patch, and a probabilistic early termination approach that exploits speckle statistics in order to speed up block matching. Finally, the use of look-up tables helps in further reducing the processing costs. The technique proposed conjugates excellent performance and low complexity, as demonstrated on both simulated and real-world SAR images and on a dedicated SAR despeckling benchmark.
165 citations
TL;DR: Based on the sea surface analysis, the proposed method cannot only efficiently block out no-candidate regions to reduce computational time, but also automatically assign weights for candidate selection function to optimize the detection performance.
Abstract: Automatic ship detection in high-resolution optical satellite images with various sea surfaces is a challenging task. In this letter, we propose a novel detection method based on sea surface analysis to solve this problem. The proposed method first analyzes whether the sea surface is homogeneous or not by using two new features. Then, a novel linear function combining pixel and region characteristics is employed to select ship candidates. Finally, Compactness and Length-width ratio are adopted to remove false alarms. Specifically, based on the sea surface analysis, the proposed method cannot only efficiently block out no-candidate regions to reduce computational time, but also automatically assign weights for candidate selection function to optimize the detection performance. Experimental results on real panchromatic satellite images demonstrate the detection accuracy and computational efficiency of the proposed method.
148 citations
TL;DR: A simple and effective unsupervised approach based on the combined difference image and k-means clustering is proposed for the synthetic aperture radar (SAR) image change detection task, and local consistency and edge information of the difference image are considered.
Abstract: In this letter, a simple and effective unsupervised approach based on the combined difference image and k-means clustering is proposed for the synthetic aperture radar (SAR) image change detection task. First, we use one of the most popular denoising methods, the probabilistic-patch-based algorithm, for speckle noise reduction of the two multitemporal SAR images, and the subtraction operator and the log ratio operator are applied to generate two kinds of simple change maps. Then, the mean filter and the median filter are used to the two change maps, respectively, where the mean filter focuses on making the change map smooth and the local area consistent, and the median filter is used to preserve the edge information. Second, a simple combination framework which uses the maps obtained by the mean filter and the median filter is proposed to generate a better change map. Finally, the k-means clustering algorithm with k = 2 is used to cluster it into two classes, changed area and unchanged area. Local consistency and edge information of the difference image are considered in this method. Experimental results obtained on four real SAR image data sets confirm the effectiveness of the proposed approach.
148 citations
TL;DR: A new energy function based on an active contour model to segment water and land and minimize it with an iterative global optimization method and unify them with a binary linear programming problem by utilizing the context information.
Abstract: In this letter, we present a new method to detect inshore ships using shape and context information. We first propose a new energy function based on an active contour model to segment water and land and minimize it with an iterative global optimization method. The proposed energy performs well on the different intensity distributions between water and land and produces a result that can be well used in shape and context analyses. In the segmented image, ships are detected with successive shape analysis, including shape analysis in the localization of ship head and region growing in computing the width and length of ship. Finally, to locate ships accurately and remove the false alarms, we unify them with a binary linear programming problem by utilizing the context information. Experiments on QuickBird images show the robustness and precision of our method.
TL;DR: Experimental results on two hyperspectral data prove that the proposed kernel collaborative representation with Tikhonov regularization technique outperforms the traditional support vector machines with composite kernels and other state-of-the-art classifiers, such as kernel sparse representation classifier and kernel collaborative representations classifier.
Abstract: In this letter, kernel collaborative representation with Tikhonov regularization (KCRT) is proposed for hyperspectral image classification. The original data is projected into a high-dimensional kernel space by using a nonlinear mapping function to improve the class separability. Moreover, spatial information at neighboring locations is incorporated in the kernel space. Experimental results on two hyperspectral data prove that our proposed technique outperforms the traditional support vector machines with composite kernels and other state-of-the-art classifiers, such as kernel sparse representation classifier and kernel collaborative representation classifier.
TL;DR: In this paper, dimensionality reduction targeting the preservation of multimodal structures is proposed to counter the parameter-space issue, where locality-preserving nonnegative matrix factorization, as well as local Fisher's discriminant analysis, is deployed as preprocessing to reduce the dimensionality of data for the Gaussian-mixture-model classifier.
Abstract: The Gaussian mixture model is a well-known classification tool that captures non-Gaussian statistics of multivariate data. However, the impractically large size of the resulting parameter space has hindered widespread adoption of Gaussian mixture models for hyperspectral imagery. To counter this parameter-space issue, dimensionality reduction targeting the preservation of multimodal structures is proposed. Specifically, locality-preserving nonnegative matrix factorization, as well as local Fisher's discriminant analysis, is deployed as preprocessing to reduce the dimensionality of data for the Gaussian-mixture-model classifier, while preserving multimodal structures within the data. In addition, the pixel-wise classification results from the Gaussian mixture model are combined with spatial-context information resulting from a Markov random field. Experimental results demonstrate that the proposed classification system significantly outperforms other approaches even under limited training data.
TL;DR: To address the model selection issue that is associated with the ELM, an automatic-solution-based differential evolution (DE) algorithm is developed that uses cross-validation accuracy as a performance indicator for determining the optimal ELM parameters.
Abstract: Recently, a new machine learning approach that is termed as the extreme learning machine (ELM) has been introduced in the literature. This approach is characterized by a unified formulation for regression, binary, and multiclass classification problems, and the related solution is given in an analytical compact form. In this letter, we propose an efficient classification method for hyperspectral images based on this machine learning approach. To address the model selection issue that is associated with the ELM, we develop an automatic-solution-based differential evolution (DE). This simple yet powerful evolutionary optimization algorithm uses cross-validation accuracy as a performance indicator for determining the optimal ELM parameters. Experimental results obtained from four benchmark hyperspectral data sets confirm the attractive properties of the proposed DE-ELM method in terms of classification accuracy and computation time.
TL;DR: The experimental results demonstrate that the proposed algorithm produces visually appealing dehazing images and retains the very fine details, and for images containing partly clear and partly hazy areas, the algorithm can also achieve good results.
Abstract: Remote sensing images are widely used in various fields. However, they usually suffer from the poor contrast caused by haze. In this letter, we propose a simple, but effective, way to eliminate the haze effect on remote sensing images. Our work is based on the dark channel prior and a common haze imaging model. In order to eliminate halo artifacts, we use a low-pass Gaussian filter to refine the coarse estimated atmospheric veil. We then redefine the transmission, with the aim of preventing the color distortion of the recovered images. The main advantage of the proposed algorithm is its fast speed, while it can also achieve good results. The experimental results demonstrate that our algorithm produces visually appealing dehazing images and retains the very fine details. Moreover, for images containing partly clear and partly hazy areas, our algorithm can also achieve good results.
TL;DR: The synchrosqueezing transform (SST) is a promising tool to provide a detailed time-frequency representation and its potential to seismic signal processing applications is shown.
Abstract: Time-frequency analysis can provide useful information in seismic data processing and interpretation. An accurate time-frequency representation is important in highlighting subtle geologic structures and in detecting anomalies associated with hydrocarbon reservoirs. The popular methods, like short-time Fourier transform and wavelet analysis, have limitations in dealing with fast varying instantaneous frequencies, which is often the characteristic of seismic data. The synchrosqueezing transform (SST) is a promising tool to provide a detailed time-frequency representation. We apply the SST to seismic data and show its potential to seismic signal processing applications.
TL;DR: A novel parameter estimation method based on keystone transform and Radon-Fourier transform for space moving targets with high-speed maneuvering performance that can overcome the limitation of Doppler frequency ambiguity and correct range curvature for all targets in one processing step, which simplifies the operation procedure.
Abstract: This letter proposes a novel parameter estimation method based on keystone transform (KT) and Radon-Fourier transform (RFT) for space moving targets with high-speed maneuvering performance. In this method, second-order KT is used to correct the range curvature and part of the range walk for all targets simultaneously. Then, fractional Fourier transform is employed to estimate the targets' radial acceleration, followed by the quadric phase term compensation. Finally, RFT and Clean technique are carried out to correct the residual range walk, and the initial range and radial velocity of moving targets are further obtained. The advantage of the proposed method is that it can overcome the limitation of Doppler frequency ambiguity and correct range curvature for all targets in one processing step, which simplifies the operation procedure. Simulation results are presented to demonstrate the validity of the proposed method.
TL;DR: In this paper, classification of various human activities based on micro-Doppler signatures is studied using linear predictive coding (LPC) to reduce the computational time cost for extracting features, which makes real-time processing feasible.
Abstract: In this letter, classification of various human activities based on micro-Doppler signatures is studied using linear predictive coding (LPC). LPC is proposed to extract the features of micro-Doppler that are mixtures of different frequencies. The use of LPC can not only decrease the time frame required to capture the Doppler signature of human motion but can also reduce the computational time cost for extracting its features, which makes real-time processing feasible. The measured data of 12 human subjects performing seven different activities using a Doppler radar are used. These activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. A support vector machine is then trained using the output of LPC to classify the activities. Multiclass classification is implemented using a one-versus-one decision structure. The resulting classification accuracy is found to be over 85%. The effects of the number of LPC coefficients and the size of the sliding time window, as well as the decision time-frame size used in the extraction of micro-Doppler signatures, are also discussed.
TL;DR: A rotation invariant parts-based model to detect objects with complex shape in high-resolution remote sensing images is proposed and the experimental results demonstrate the robustness and precision of the proposed detection model.
Abstract: In this letter, we propose a rotation invariant parts-based model to detect objects with complex shape in high-resolution remote sensing images. Specifically, the geospatial objects with complex shape are firstly divided into several main parts, and the structure information among parts is described and regulated in polar coordinates to achieve the rotation invariance on configuration. Meanwhile, the pose variance of each part relative to the object is also defined in our model. In encoding the features of the rotated parts and objects, a new rotation invariant feature is proposed by extending histogram oriented gradients. During the final detection step, a clustering method is introduced to locate the parts in objects, and that method can also be used to fuse the detection results. By this way, an efficient detection model is constructed and the experimental results demonstrate the robustness and precision of our proposed detection model.
TL;DR: A novel framework that generalizes well-established pan-sharpening algorithms to HSI and MSI fusion of the same scene collected by the coupled sensors is presented and results show that the proposed methods excel the state-of-the-art methods in terms of simplicity, feasibility, efficiency, and effectiveness.
Abstract: In many applications, it is imperative to maintain high spectral and spatial resolution of remote sensing images. This letter addresses the issue by fusing low-spatial-resolution hyperspectral images (HSIs) and high-spatial-resolution multispectral images (MSIs) of the same scene collected by the coupled sensors and, thus, present a novel framework that generalizes well-established pan-sharpening algorithms. The main steps of the framework are dividing the spectrum of HSIs into several regions and fusing HSIs and MSIs in each region by the chosen pan-sharpening algorithm. Ratio image-based spectral resampling (RIBSR) is used to interpolate the missing data so that every region is covered by a multispectral band. Therefore, the framework allows most of pan-sharpening algorithms to be extended to HSI and MSI fusion. Synthetic data in accordance with sensor reality are used to test specific methods derived within the framework. Experimental results show that the proposed methods excel the state-of-the-art methods in terms of simplicity, feasibility, efficiency, and effectiveness.
TL;DR: This letter reviews and compares several structured priors for sparse-representation-based HSI classification and proposes a new structured prior called the low-rank (LR) group prior, which can be considered as a modification of the LR prior.
Abstract: Pixelwise classification, where each pixel is assigned to a predefined class, is one of the most important procedures in hyperspectral image (HSI) analysis. By representing a test pixel as a linear combination of a small subset of labeled pixels, a sparse representation classifier (SRC) gives rather plausible results compared with that of traditional classifiers such as the support vector machine. Recently, by incorporating additional structured sparsity priors, the second-generation SRCs have appeared in the literature and are reported to further improve the performance of HSI. These priors are based on exploiting the spatial dependences between the neighboring pixels, the inherent structure of the dictionary, or both. In this letter, we review and compare several structured priors for sparse-representation-based HSI classification. We also propose a new structured prior called the low-rank (LR) group prior, which can be considered as a modification of the LR prior. Furthermore, we will investigate how different structured priors improve the result for the HSI classification.
TL;DR: The results indicate that the MLS system has the potential to accurately map large forest plots and further research on mapping accuracy and cost-benefit analyses is needed.
Abstract: Terrestrial laser scanning (TLS) has been demonstrated to be an efficient measurement method in plot-level forest inventories. A permanent sample plot in national forest inventories is typically a small area of forest with a radius of approximately 10 m. In practice, whether reference data can be automatically and accurately collected for larger plot sizes is of great interest. It is expensive to collect references in large areas utilizing conventional measurement tools. The application of static TLS is a possible choice but is very challenging due to its lack of mobility. In this letter, a mobile laser scanning (MLS) system was tested and its implications for forest inventories were discussed. The system is composed of a high performance laser scanner, a navigation unit, and a six-wheeled all-terrain vehicle. In this experiment, about 0.4 ha forest area was mapped utilizing the MLS system. The stem mapping accuracy was 87.5%; the root mean square errors of the estimations of the diameter at breast height and the location were 2.36 cm and 0.28 m, respectively. These results indicate that the MLS system has the potential to accurately map large forest plots and further research on mapping accuracy and cost-benefit analyses is needed.
TL;DR: Simulation results show that the proposed algorithm improves the performance for detecting and imaging high-speed maneuvering targets and theoretical analysis confirms that the methodology can precisely focus targets.
Abstract: Weak-target detection and imaging are the challenging problems of airborne or spaceborne early warning radar. The envelope of a high-speed weak target after range compression spreads over range during the long observation period. To finely refocus a high-speed weak maneuvering target, motion parameters should be accurately obtained for compensating the envelope. This letter proposes a new imaging approach for high-speed maneuvering targets without a priori knowledge of their motion parameters. In this method, the azimuth compression function is constructed in a range and azimuth 2-D frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets. Simulation results show that the proposed algorithm improves the performance for detecting and imaging high-speed maneuvering targets.
TL;DR: Numerical simulations show that by combining first-order keystone transform and azimuth NLCS operation, the raw data of ST-BFSAR can be well imaged.
Abstract: With appropriate geometry configurations, bistatic synthetic aperture radar (SAR) can break through the limitations of monostatic SAR on forward-looking imaging. Thanks to such a capability, bistatic forward-looking SAR (BFSAR) has extensive potential applications, such as self-navigation and self-landing. In the mode of BFSAR with a stationary transmitter (ST-BFSAR), the two-dimensional spatial variation makes it difficult to use traditional data focusing algorithms. In this letter, an imaging algorithm based on keystone transform and nonlinear chirp scaling (NLCS) is proposed to deal with this problem. Keystone transform is used to remove the spatial variation of range cell migration. NLCS can eliminate the variation of azimuth reference function. Numerical simulations show that by combining first-order keystone transform and azimuth NLCS operation, the raw data of ST-BFSAR can be well imaged.
TL;DR: Experiments carried out on QuickBird and IKONOS satellite images show that the IAIHS method can maintain spectral quality while providing comparable spatial quality with the AIHS and additive wavelet luminance proportional methods.
Abstract: Extending on the adaptive intensity-hue-saturation (AIHS) method, an improved AIHS (IAIHS) method is proposed for pansharpening in this letter. Through the IAIHS method, the amount of spatial details injected into each band of the multispectral (MS) image is appropriately determined by a weighting matrix, which is defined on the basis of the edges of the panchromatic and MS images and the proportions between the MS bands. Experiments carried out on QuickBird and IKONOS satellite images show that the IAIHS method can maintain spectral quality while providing comparable spatial quality with the AIHS and additive wavelet luminance proportional methods.
TL;DR: The so-called variational heteroscedastic GPR (VHGPR) is shown to be an excellent alternative to standard GPR in two relevant Earth observation examples, namely, Chl vegetation retrieval from hyperspectral images and oceanic Chl concentration estimation from in situ measured reflectances.
Abstract: An accurate estimation of biophysical variables is the key to monitor our Planet. Leaf chlorophyll content helps in interpreting the chlorophyll fluorescence signal from space, whereas oceanic chlorophyll concentration allows us to quantify the healthiness of the oceans. Recently, the family of Bayesian nonparametric methods has provided excellent results in these situations. A particularly useful method in this framework is the Gaussian process regression (GPR). However, standard GPR assumes that the variance of the noise process is independent of the signal, which does not hold in most of the problems. In this letter, we propose a nonstandard variational approximation that allows accurate inference in signal-dependent noise scenarios. We show that the so-called variational heteroscedastic GPR (VHGPR) is an excellent alternative to standard GPR in two relevant Earth observation examples, namely, Chl vegetation retrieval from hyperspectral images and oceanic Chl concentration estimation from in situ measured reflectances. The proposed VHGPR outperforms the tested empirical approaches, as well as statistical linear regression (both least squares and least absolute shrinkage and selection operator), neural nets, and kernel ridge regression, and the homoscedastic GPR, in terms of accuracy and bias, and proves more robust when a low number of examples is available.
TL;DR: The performance of SS-LapSVM is evaluated on AVIRIS image data taken over Indiana's Indian Pine, and the results show that it can achieve accurate and rapid classification with a small number of labeled data, and outperform state-of-the-art semi-supervised approaches.
Abstract: In this letter, we propose a new spatio-spectral Laplacian support vector machine (SS-LapSVM) for semi-supervised hyperspectral image classification. The clustering assumption on spectral vectors is used to formulate a manifold regularizer, and neighborhood spatial constraints of hyperspectral images are designed to construct a spatial regularizer. Moreover, a non-iterative optimization procedure is presented to solve this dual-regularized SVM, which makes rapid classification possible. By combining spatial and spectral information together, SS-LapSVM can avoid the speckle-like misclassification of hyperspectral images in the original Lap-SVM. The performance of SS-LapSVM is evaluated on AVIRIS image data taken over Indiana's Indian Pine, and the results show that it can achieve accurate and rapid classification with a small number of labeled data, and outperform state-of-the-art semi-supervised approaches.
TL;DR: The ecohydrological wireless sensor network (EHWSN), which has been installed in the middle reach of the Heihe River Basin, provides superior integrated, standardized, and automated observation capabilities for hydrological and ecological processes research at the basin scale.
Abstract: This letter introduces the ecohydrological wireless sensor network (EHWSN), which we have installed in the middle reach of the Heihe River Basin. The EHWSN has two primary objectives: the first objective is to capture the multiscale spatial variations and temporal dynamics of soil moisture, soil temperature, and land surface temperature in the heterogeneous farmland; and the second objective is to provide a remote-sensing ground-truth estimate with an approximate kilometer pixel scale using spatial upscaling. This ground truth can be used for validation and evaluation of remote-sensing products. The EHWSN integrates distributed observation nodes to achieve an automated, intelligent, and remote-controllable network that provides superior integrated, standardized, and automated observation capabilities for hydrological and ecological processes research at the basin scale.
TL;DR: Experimental results indicated that the proposed approach outperforms existing methods in terms of objective criteria and subjective perception improving the image resolution.
Abstract: This letter addresses the problem of generating a super-resolution (SR) image from a single low-resolution (LR) input image in the wavelet domain. To achieve a sharper image, an intermediate stage for estimating the high-frequency (HF) subbands has been proposed. This stage includes an edge preservation procedure and mutual interpolation between the input LR image and the HF subband images, as performed via the discrete wavelet transform (DWT). Sparse mixing weights are calculated over blocks of coefficients in an image, which provides a sparse signal representation in the LR image. All of the subband images are used to generate the new high-resolution image using the inverse DWT. Experimental results indicated that the proposed approach outperforms existing methods in terms of objective criteria and subjective perception improving the image resolution.
TL;DR: The experimental results show that the improved optimizing algorithm proposed can be implemented with time-saving, high-precision ship extraction, feature analysis, and detection.
Abstract: High-resolution synthetic aperture radar (SAR) data have been widely used in marine environmental protection, marine environmental monitoring, and marine traffic management. Ship detection is one of the important parts of SAR data for marine applications. This letter focuses on the feature analysis of ships in high-resolution SAR images and proposes an improved optimizing algorithm for ship detection. A fast block detector is designed to extract sea clutter in a uniform local area, and then a constant false alarm rate detector is employed. Based on the kernel density estimation of ships, aspect ratio, and pixel points, ships are identified. TerraSAR-X and COSMO-SkyMed images are used to test our algorithm. The experimental results show that this algorithm can be implemented with time-saving, high-precision ship extraction, feature analysis, and detection.