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| 2. |
- Alves, Dimas I, et al.
(författare)
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A Statistical Analysis for Wavelength-Resolution SAR Image Stacks
- 2020
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1545-598X .- 1558-0571. ; 17:2, s. 227-231
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Tidskriftsartikel (refereegranskat)abstract
- This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59 & x0025; of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.
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| 3. |
- Alves, Dimas I., et al.
(författare)
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Neyman-Pearson Criterion-Based Change Detection Methods for Wavelength-Resolution SAR Image Stacks
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-598X .- 1558-0571. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman-Pearson criterion. The first proposed method uses the data from wavelength-resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considers a priori information about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods. CCBY
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| 4. |
- Askne, Jan, 1936, et al.
(författare)
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On the Estimation of Boreal Forest Biomass From TanDEM-X Data Without Training Samples
- 2015
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1558-0571 .- 1545-598X. ; 12:4, s. 771-775
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Tidskriftsartikel (refereegranskat)abstract
- Boreal forests play an important part in the climate system, and estimates of the biomass are important also from an economic point of view. In this letter, forest aboveground biomass is estimated from bistatic TanDEM-X data, a Lidar digital elevation model (DEM), and the interferometric water cloud model, without using training samples to calibrate the model. The forest was characterized by allometric relations for area fill (vegetation fraction) and height versus stem volume, and stem volume versus biomass. Biomass was estimated for 202 forest stands at least 1 ha large at the forest test site of Remningstorp, Sweden, from 18 bistatic TanDEM-X acquisitions with a relative root-mean-square error (RMSE) of 16%-32%. TanDEM-X acquisitions with a height of ambiguity around 80 m resulted in the best results. A multitemporal combination resulted in a relative RMSE of 17%. This result is comparable with the retrieval error obtained in a previous study when training the model using a set of known forest stands.
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| 5. |
- Berg, Anders, 1983, et al.
(författare)
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SAR Algorithm for Sea Ice Concentration - Evaluation for the Baltic Sea
- 2012
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1558-0571 .- 1545-598X. ; 9:5, s. 938 - 942
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Tidskriftsartikel (refereegranskat)abstract
- A new sea ice concentration algorithm has been developed for C-band synthetic aperture radar data. Detailed autocorrelation statistics are derived and adopted by the algorithm, and a neural network is utilized for training against 41 sea ice charts. The charts are produced by ice analysts at the Swedish Ice Service and cover the Baltic Sea. The classification of open water pixels is accurate to 94% on average, and the classification of sea ice pixels has an accuracy of 87%. This results in a root-mean-square error of 6.7 percentage points in estimating the sea ice concentration.
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| 6. |
- Blixt, E. M., et al.
(författare)
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Optical flow analysis of the aurora borealis
- 2006
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-598X .- 1558-0571. ; 3:1, s. 159-163
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Tidskriftsartikel (refereegranskat)abstract
- Optical observations of the aurora have traditionally focused on the structure, intensity, and wavelength of the emissions. But the apparent motion of auroral forms offers another important diagnostic tool for investigating this poorly understood phenomenon. Prior analyses of auroral motion have focused on tracing individual features. In this letter, we investigate the feasibility of deriving the entire two-dimensional velocity field automatically using robust optical flow estimation. The analysis is applied to two narrow-field video sequences. Both examples are rich in small-scale structure and motion, but appear very different to the eye. The robust optical flow estimator performed well for regions of dense turbulent motion, while sheared flow and flow which is perpendicular to image intensity gradients, was poorly resolved. The relative magnitude of the outliers provides a quantitative measure of the validity of the underlying flow model and, hence, a means of automatically differentiating among auroral forms with differing physical origins. The technique can be used to deduce ionospheric electric fields and neutral winds, and the flow fields yield important physical information about the generation mechanism.
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| 7. |
- Blomberg, Erik, 1987, et al.
(författare)
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Forest Biomass Retrieval from L-Band SAR Using Tomographic Ground Backscatter Removal
- 2018
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - 1558-0571 .- 1545-598X. ; 15:7, s. 1030-1034
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Tidskriftsartikel (refereegranskat)abstract
- A tomographic synthetic aperture radar (TomoSAR) represents a possible route to improved retrievals of forest parameters. Simulated orbital L-band TomoSAR data corresponding to the proposed Satellites for Observation and Communications-Companion Satellite (SAOCOM-CS) mission (1.275 GHz) are evaluated for retrieval of above-ground biomass in boreal forest. L-band data and biomass measurements, collected at the Krycklan test site in northern Sweden as part of the BioSAR 2008 campaign, are used to compare biomass retrievals from SAOCOM-CS to those based on SAOCOM SAR data. Both data sets are in turn compared with the corresponding airborne case, as represented by experimental airborne SAR through processing of the original SAR data. TomoSAR retrievals use a model involving a logarithmic transform of the volumetric backscatter intensity, Ivol, defined as the total backscatter originating between 10 and 30 m above ground. SAR retrievals are obtained with slope-compensated intensity γ0using the same model. It is concluded that tomography using SAOCOM-CS represents an improvement over an airborne SAR imagery, resulting in biomass retrievals from a single polarization (HH) having a 26%-30% root-mean-square error with a little to no impact from the look direction or the local topography.
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| 8. |
- Campos, Alexandre Becker, 1997-, et al.
(författare)
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False Alarm Reduction in Wavelength-Resolution SAR Change Detection Schemes by Using a Convolutional Neural Network
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-598X .- 1558-0571. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we propose a method to reduce the number of false alarms in a wavelength-resolution synthetic aperture radar (SAR) change detection scheme by using a convolutional neural network (CNN). The detection is performed in two steps: change analysis and object classification. A simple technique for wavelength-resolution SAR change detection is implemented to extract potential targets from the image of interest. A CNN is then used for classifying the change map detections as either a target or nontarget, further reducing the false alarm rate (FAR). The scheme is tested for the CARABAS-II data set, where only three false alarms over a testing area of 96 km² are reported while still sustaining a probability of detection above 96%. We also show that the network can still reduce the FAR even when the flight heading of the SAR system measurement campaign differs by up to 100° between the images used for training and test. CCBY
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| 9. |
- Diaw, Moustapha, et al.
(författare)
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Optical Aerial Images Change Detection Based on a Color Local Dissimilarity Map and k-Means Clustering
- 2022
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE. - 1545-598X .- 1558-0571. ; 19
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Tidskriftsartikel (refereegranskat)abstract
- Considering the unavailability of labeled data sets in remote sensing change detection, this letter presents a novel and low complexity unsupervised change detection method based on the combination of similarity and dissimilarity measures: mutual information (MI), disjoint information (DI), and local dissimilarity map (LDM). MI and DI are calculated on sliding windows with a step of 1 pixel for each pair of channels of both images. The resulting scalar values, weighted by q and m coefficients, are multiplied by the values of the center pixels of the windows weighted by p to remove the textures on images. The changes are detected using, respectively, the grayscale LDM and color LDM. A sliding window is then used on the color LDM and each pixel is characterized by a two-parameter Weibull distribution. Binarized change maps can be obtained by using a k-means clustering on the model parameters. Experiments on optical aerial image data set show that the proposed method produces comparable, even better results, to the state-of-the-art methods in terms of recall, precision, and F-measure.
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| 10. |
- Gomes, Natanael Rodrigues, et al.
(författare)
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Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection
- 2019
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Ingår i: IEEE Geoscience and Remote Sensing Letters. - : IEEE. - 1545-598X .- 1558-0571. ; 16, s. 756-760
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this letter is to compare two incoherent change-detection algorithms for target detection in low-frequency ultrawideband (UWB) synthetic aperture radar (SAR) images. The considered UWB SAR operates in the frequency range from 20 to 90 MHz. Both approaches employ a likelihood ratio test according to the Neyman–Pearson criterion. First, the bivariate Rayleigh probability distribution is used to implement the likelihood ratio test function. This distribution is well known and has been used for change-detection algorithms in low-frequency UWB SAR with good results. Aiming to minimize the false alarm rate and taking into consideration that low-frequency UWB SAR images have high resolution compared to the transmitted wavelength, the second approach implements the test by using a bivariate K-distribution. This distribution has scale and shape parameters that can be used to adjust it to the data. No filter is applied to the data set images, and the results show that with a good statistical model, it is not needed to rely on filtering the data to decrease the number of false alarms. Therefore, we can have a better tradeoff between resolution and detection performance.
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