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Alves, D. I., Palm, B., Pettersson, M., Vu, V. T., Machado, R., Uchoa-Filho, B. F., . . . Hellsten, H. (2020). A Statistical Analysis for Wavelength-Resolution SAR Image Stacks. IEEE Geoscience and Remote Sensing Letters, 17(2), 227-231
Open this publication in new window or tab >>A Statistical Analysis for Wavelength-Resolution SAR Image Stacks
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2020 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 17, no 2, p. 227-231Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020
Keywords
Synthetic aperture radar, Radar polarimetry, Image resolution, Data models, Statistical analysis, Rician channels, Geometry, Anderson-Darling (AD) test, CARABAS II, change detection, image stack, multitemporal synthetic aperture radar (SAR) images, SAR
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Remote Sensing
Identifiers
urn:nbn:se:bth-19226 (URN)10.1109/LGRS.2019.2917627 (DOI)000510900300009 ()
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-20Bibliographically approved
Javadi, M. S., Dahl, M. & Pettersson, M. (2020). Adjustable Contrast Enhancement Using Fast Piecewise Linear Histogram Equalization. In: ACM International Conference Proceeding Series: . Paper presented at 3rd International Conference on Image and Graphics Processing, ICIGP 2020; Singapore; 8 February 2020 through 10 February 2020 (pp. 57-61). Association for Computing Machinery
Open this publication in new window or tab >>Adjustable Contrast Enhancement Using Fast Piecewise Linear Histogram Equalization
2020 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2020, p. 57-61Conference paper, Published paper (Refereed)
Abstract [en]

Histogram equalization is a technique to enhance the contrast of the image by redistributing the histogram. In this paper, a fast piecewise linear histogram equalization method is introduced based on an adjustable degree of enhancement and piecewise continuous transformation functions using frequencies of different grey-levels. This method aims to address and maximize the contrast enhancement of the image by stretching the entire spectrum. For this purpose, particular nodes (bins) on the histogram are simultaneously detected that in comparison with recursive methods, it requires less computational time. Then, the particular nodes are stretched using transformation functions to align with the reference nodes. The experimental results indicate that the performance of the proposed method is promising in terms of contrast enhancement. Moreover, this method preserves the texture of various regions in the image very well through the equalization process by using the degree of enhancement. © 2020 Owner/Author.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2020
Keywords
Contrast enhancement, Histogram equalization, Histogram modification, Image/video enhancement, Equalizers, Graphic methods, Linear transformations, Piecewise linear techniques, Textures, Computational time, Histogram equalizations, Piecewise linear, Piecewise-continuous, Recursive methods, Reference nodes, Transformation functions, Image enhancement
National Category
Control Engineering
Identifiers
urn:nbn:se:bth-19394 (URN)10.1145/3383812.3383830 (DOI)2-s2.0-85083108101 (Scopus ID)9781450377201 (ISBN)
Conference
3rd International Conference on Image and Graphics Processing, ICIGP 2020; Singapore; 8 February 2020 through 10 February 2020
Note

Sponsor: Nanyang Technological University,University of Bologna (UNIBO)

Open Access

Available from: 2020-04-24 Created: 2020-04-24 Last updated: 2020-04-24Bibliographically approved
Javadi, M. S., Dahl, M. & Pettersson, M. (2020). Change Detection in Aerial Images Using Three-Dimensional Feature Maps. Remote Sensing, 12(9), Article ID 1404.
Open this publication in new window or tab >>Change Detection in Aerial Images Using Three-Dimensional Feature Maps
2020 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 12, no 9, article id 1404Article in journal (Refereed) Published
Abstract [en]

 Interest in aerial image analysis has increased owing to recent developments in and availabilityofaerialimagingtechnologies,likeunmannedaerialvehicles(UAVs),aswellasagrowing need for autonomous surveillance systems. Variant illumination, intensity noise, and different viewpointsareamongthemainchallengestoovercomeinordertodeterminechangesinaerialimages. In this paper, we present a robust method for change detection in aerial images. To accomplish this, the method extracts three-dimensional (3D) features for segmentation of objects above a defined reference surface at each instant. The acquired 3D feature maps, with two measurements, are then used to determine changes in a scene over time. In addition, the important parameters that affect measurement, such as the camera’s sampling rate, image resolution, the height of the drone, and the pixel’sheightinformation,areinvestigatedthroughamathematicalmodel. Toexhibititsapplicability, the proposed method has been evaluated on aerial images of various real-world locations and the results are promising. The performance indicates the robustness of the method in addressing the problems of conventional change detection methods, such as intensity differences and shadows.

Keywords
aerial images; 3D change detection; optical vehicle surveillance; remote sensing; unmanned aerial vehicle
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:bth-19422 (URN)10.3390/rs12091404 (DOI)
Note

open access

Available from: 2020-05-01 Created: 2020-05-01 Last updated: 2020-05-04Bibliographically approved
Rameez, M., Dahl, M. & Pettersson, M. (2020). Experimental Evaluation of Adaptive Beamforming for Automotive Radar Interference Suppression. In: IEEE Radio and Wireless Symposium, RWS: . Paper presented at IEEE Radio and Wireless Symposium, RWW 2020; San Antonio; United States; 26 January 2020 through 29 January 2020 (pp. 183-186). IEEE, Article ID 9049982.
Open this publication in new window or tab >>Experimental Evaluation of Adaptive Beamforming for Automotive Radar Interference Suppression
2020 (English)In: IEEE Radio and Wireless Symposium, RWS, IEEE, 2020, p. 183-186, article id 9049982Conference paper, Published paper (Refereed)
Abstract [en]

Mutual interference between automotive radars can make it difficult to detect targets, especially the weaker ones, such as cyclists and pedestrians. In this paper, the interference suppression performance of a Least Mean Squares (LMS) algorithm-based adaptive beamformer is evaluated using measurements from a 77 GHz Frequency Modulated Continuous Wave (FMCW) radar in an outdoor environment. It is shown that the adaptive beamformer increases detection performance and that the interference is suppressed down to the noise floor of the radar in the Range-Doppler domain. In the paper, real baseband sampling and complex-baseband sampling (IQ) radar receivers are compared in the context of interference suppression. The measurements show that IQ receivers are more beneficial in the presence of interference.

Place, publisher, year, edition, pages
IEEE, 2020
Keywords
Automotive radar, Frequency Modulated Continuous Wave (FMCW), interference mitigation, digital beamforming
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-19368 (URN)10.1109/RWS45077.2020.9049982 (DOI)
Conference
IEEE Radio and Wireless Symposium, RWW 2020; San Antonio; United States; 26 January 2020 through 29 January 2020
Note

Sponsorer: AESS,APS,IEEE,MTT-S

Available from: 2020-04-07 Created: 2020-04-07 Last updated: 2020-04-24Bibliographically approved
G Palm, B., Alves, D., Pettersson, M., Vu, V. T., Machado, R., J Cintra, R., . . . Hellsten, H. (2020). Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack. Sensors, 20(7)
Open this publication in new window or tab >>Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack
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2020 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 7Article in journal (Refereed) Published
Abstract [en]

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0 . 11 / km 2 , when considering military vehicles concealed in a forest.

Place, publisher, year, edition, pages
NLM (Medline), 2020
Keywords
CARABAS II, ground scene prediction, image stack, multi-pass, SAR images, army, article, detection algorithm, forest, geometry, prediction, probability, telecommunication
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-19389 (URN)10.3390/s20072008 (DOI)2-s2.0-85083022547 (Scopus ID)
Note

Open access

Available from: 2020-04-17 Created: 2020-04-17 Last updated: 2020-05-04Bibliographically approved
Molin, R. D. ., Rosa, R. A. S., Bayer, F. M., Pettersson, M. & Machado, R. (2019). A CHANGE DETECTION ALGORITHM FOR SAR IMAGES BASED ON LOGISTIC REGRESSION. In: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019): . Paper presented at IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 28-AUG 02, 2019, Yokohama, JAPAN (pp. 1514-1517). IEEE
Open this publication in new window or tab >>A CHANGE DETECTION ALGORITHM FOR SAR IMAGES BASED ON LOGISTIC REGRESSION
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2019 (English)In: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), IEEE , 2019, p. 1514-1517Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, flight heading and neighboring features such as local mean, standard deviation and skewness. The proposed method intends to explore the advantadges from both pixel- and object-based approaches, while evaluating multiple features in amplitude only SAR images. Preliminary results based on K-fold cross validation have shown that the proposed CDA achieves good performance when compared to the results presented in [1].

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE International Symposium on Geoscience and Remote Sensing IGARSS, ISSN 2153-6996
Keywords
Change Detection Algorithm, Logistic Regression, SAR images
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-19384 (URN)000519270601187 ()978-1-5386-9154-0 (ISBN)
Conference
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 28-AUG 02, 2019, Yokohama, JAPAN
Available from: 2020-04-16 Created: 2020-04-16 Last updated: 2020-04-16Bibliographically approved
Vu, V. T., Alves, D., Palm, B., Pettersson, M., Dammert, P. & Hellsten, H. (2019). A detector for wavelength resolution SAR incoherent change detection. In: 2019 IEEE Radar Conference, RadarConf 2019: . Paper presented at IEEE Radar Conference, RadarConf, Boston, 22 April through 26 April 2019. Institute of Electrical and Electronics Engineers Inc., Article ID 8835574.
Open this publication in new window or tab >>A detector for wavelength resolution SAR incoherent change detection
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2019 (English)In: 2019 IEEE Radar Conference, RadarConf 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, article id 8835574Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces an effective detector for wavelength-resolution SAR incoherent change detection. The detector is derived from Bayes' theorem. The input of the detector is the differences between surveillance and reference magnitude images simply obtained by a subtraction while the output is a summary of the detected changes. The proposed detector is tested with 24 CARABAS images that were obtained from the measurement campaign in northern Sweden in 2002. The testing results show that the detector can provide a high average detection probability, e.g., about 96%, with a very low false alarm rate, e.g., only 0.35 per square kilometer. © 2019 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Radar imaging, Bayes' theorem, CARABAS, Change detection, Detection probabilities, False alarm rate, Measurement campaign, Northern sweden, Wavelength resolution, Synthetic aperture radar
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-18815 (URN)10.1109/RADAR.2019.8835574 (DOI)2-s2.0-85073100690 (Scopus ID)9781728116792 (ISBN)
Conference
IEEE Radar Conference, RadarConf, Boston, 22 April through 26 April 2019
Available from: 2019-10-31 Created: 2019-10-31 Last updated: 2019-10-31Bibliographically approved
Vu, V. T., Pettersson, M., Dahl, M. & Sjögren, T. K. (2019). A MEASUREMENT CAMPAIGN IN HARBOR TO DETECT CHANGES OF ACTIVITIES. In: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019): . Paper presented at IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 28-AUG 02, 2019, Yokohama, JAPAN (pp. 1494-1497). IEEE
Open this publication in new window or tab >>A MEASUREMENT CAMPAIGN IN HARBOR TO DETECT CHANGES OF ACTIVITIES
2019 (English)In: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), IEEE , 2019, p. 1494-1497Conference paper, Published paper (Refereed)
Abstract [en]

The typical activities in a harbor mainly concern logistics, traffic and transportation that need to be monitored or optimized. This paper reports a measurement campaign whose goal is to evaluate the performance of activity surveillance in a harbor using satellite SAR systems. The measurements were conducted in the southern Baltic sea, around the Verko harbor in Karlskrona, Sweden in the middle of 2018. The SAR data acquisition was with TerraSAR-X/TanDEM-X operating in staring spotlight mode as high resolution was desired. For an accurate evaluation, all activities in the Verko harbor were video recorded by a drone during the satellite measurement time. Some radar corner reflectors were also deployed on the ground that are available for co-registration. The data associated with two measurements in the end of August and in the beginning of September is presented in this paper. The SAR change detection processing and the initial analysis show the possibility to monitor activities concerning logistics, traffic and transportation using satellite SAR.

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE International Symposium on Geoscience and Remote Sensing IGARSS, ISSN 2153-6996
Keywords
Transport monitoring, TerraSAR-X/TanDEM-X, Bayes' theorem, multivariate distribution
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Remote Sensing
Identifiers
urn:nbn:se:bth-19386 (URN)000519270601182 ()978-1-5386-9154-0 (ISBN)
Conference
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 28-AUG 02, 2019, Yokohama, JAPAN
Available from: 2020-04-16 Created: 2020-04-16 Last updated: 2020-04-16Bibliographically approved
Vu, V. T., Gomes, N. R., Pettersson, M., Dämmert, P. & Hellsten, H. (2019). Bivariate Gamma Distribution for Wavelength-Resolution SAR Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 473-481
Open this publication in new window or tab >>Bivariate Gamma Distribution for Wavelength-Resolution SAR Change Detection
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2019 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 57, no 1, p. 473-481Article in journal (Refereed) Published
Abstract [en]

A gamma probability density function (pdf) is shown to be an alternative to model the distribution of the magnitudes of high-resolution, i.e., wavelength-resolution, synthetic aperture radar (SAR) images. As investigated in this paper, it is more appropriate and more realistic statistical in comparison with, e.g., Rayleigh. A bivariate gamma pdf is considered for developing a statistical hypothesis test for wavelength-resolution incoherent SAR change detection. The practical issues in implementation of statistical hypothesis test, such as assumptions on target magnitudes, estimations for scale and shape parameters, and implementation of modified Bessel function, are addressed. This paper also proposes a simple processing scheme for incoherent change detection to validate the proposed statistical hypothesis test. The proposal was experimented with 24 CARABAS data sets. With an average detection probability of 96%, the false alarm rate is only 0.47 per square kilometer. IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Bivariate gamma, CARABAS, change detection, synthetic aperture radar (SAR)., Probability density function, Probability distributions, Radar imaging, Statistical tests, Tracking radar, Bivariate, Bivariate gamma distribution, Probability density function (pdf), Scale and shape parameters, Statistical hypothesis test, Synthetic aperture radar (SAR) images, Synthetic aperture radar
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:bth-16936 (URN)10.1109/TGRS.2018.2856926 (DOI)000455089000036 ()2-s2.0-85051396922 (Scopus ID)
Available from: 2018-08-24 Created: 2018-08-24 Last updated: 2019-01-28Bibliographically approved
Gomes, N. R., Dammert, P., Pettersson, M., Vu, V. T. & Hellsten, H. (2019). Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection. IEEE Geoscience and Remote Sensing Letters, 16, 756-760
Open this publication in new window or tab >>Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection
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2019 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 16, p. 756-760Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Change detection, likelihood ratio test, synthetic aperture radar (SAR).
National Category
Signal Processing
Identifiers
urn:nbn:se:bth-17863 (URN)10.1109/LGRS.2018.2881733 (DOI)000466228400019 ()
Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2019-06-14Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6643-312x

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