WO2010112426A1 - Process for identifying statistically homogeneous pixels in sar images acquired on the same area - Google Patents
Process for identifying statistically homogeneous pixels in sar images acquired on the same area Download PDFInfo
- Publication number
- WO2010112426A1 WO2010112426A1 PCT/EP2010/054016 EP2010054016W WO2010112426A1 WO 2010112426 A1 WO2010112426 A1 WO 2010112426A1 EP 2010054016 W EP2010054016 W EP 2010054016W WO 2010112426 A1 WO2010112426 A1 WO 2010112426A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- pixels
- pixel
- sample
- amplitude
- identifying
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 230000008569 process Effects 0.000 title claims abstract description 42
- 239000013598 vector Substances 0.000 claims abstract description 59
- 238000012360 testing method Methods 0.000 claims abstract description 18
- 238000000528 statistical test Methods 0.000 claims abstract description 8
- 238000005315 distribution function Methods 0.000 claims abstract description 6
- 238000001276 Kolmogorov–Smirnov test Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims 2
- 238000012952 Resampling Methods 0.000 abstract description 8
- 238000001914 filtration Methods 0.000 description 10
- 230000003044 adaptive effect Effects 0.000 description 7
- 238000002310 reflectometry Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000005305 interferometry Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- CCEKAJIANROZEO-UHFFFAOYSA-N sulfluramid Chemical group CCNS(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F CCEKAJIANROZEO-UHFFFAOYSA-N 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20004—Adaptive image processing
- G06T2207/20012—Locally adaptive
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20076—Probabilistic image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
Definitions
- the present invention relates to a process for identifying statistically homogeneous pixels in SAR images acquired on the same area.
- a synthetic aperture radar (or SAR) system produces a two-dimensional image.
- One dimension of the image is called the range and is the measurement of the distance in a view line from the radar of the object that is being illuminated.
- the other dimension is called the azimuth and is perpendicular to the range.
- the SAR radar operates at a frequency that is generally comprised between 400 Mhz and 10 Ghz, and is usually installed on aeroplanes or satellite platforms orbiting at a height of between 250 and 800 Km.
- the antenna of the radar points to earth orthogonally to the direction of motion of the platform (aeroplane or satellite) at an angle known as the "offhadir" angle comprised between 20 and 80 degrees compared with the nadir direction, i.e. perpendicularly to the earth.
- the SAR is a consistent sensor and therefore the images are matrices of complex numbers the amplitude values of which are linked to the power that is backscattered by the illuminated objects (i.e. to the radar cross section thereof), whilst the step is determined by the nature of the target and the distance of the target from the radar.
- the SAR images are suitable for different applications; amongst these, the applications linked to the identification and classification of targets, "change detection" and interferometry applications are of primary importance. The latter are usually aimed at obtaining digital elevation models and/or the analysis of surface deformation of the terrain from sets of multitemporal SAR data.
- a general SAR image gathers data coming from targets of various natures: natural (woods, rocks, meadows, etc) or artificial (manufactured products, metal structures, motor vehicles, etc).
- the electromagnetic features can thus vary significantly even between adjacent pixels of a radar image.
- the object of the present invention is to provide a process for identifying statistically homogeneous pixels of SAR images acquired on the same area, i.e. characterised by similar electromagnetic properties, on which it is possible to make reliable statistical estimates, on a support selected in an adaptive manner.
- this object is achieved by a process for identifying statistically homogeneous pixels in images acquired on the same area by means of a synthetic aperture radar (SAR sensor) comprising the following steps: (a) acquiring a plurality of N radar images by means of a SAR sensor on the same area with acquisition geometries that are suitable to consent the common grid resampling of data,
- the process according to the invention also enables the response of the punctiform radar targets to be preserved.
- figure 1 shows a series of SAR images that are acquired and resampled on a common grid
- figure 2 show schematically an estimation window with pixels that are homogenous with the sample pixel, but are not connected to the sample pixel and pixels that are homogenous with and connected to the sample pixel
- figure 3 shows the average reflectivity map of the area of the Etna volcano
- figure 4 shows for each pixel the number of statistically homogeneous pixels associated therewith, according to the process of the invention
- figure 5a shows an image before the anti-speckle filter is applied according to the process of the invention
- figure 5b shows the same image of the figure 5a after the application of the anti-speckle filter according to the process of the invention
- figure 6a shows an interferogram before the application of the filter for inter fero grams according to the process of the invention
- figure 6b shows the same image as figure 5a after the application of the filter
- the process according to the invention works on collections (or datasets) of N radar images A 1..AN (figure 1) relating to the same area, detected at different times and/or at different view angles, but such as to consent the common grid resampling of data. Downstream of the resampling, all the dataset images are made to be superimposable on one another on a common grid, such that homologous pixels in two general images of the set of data correspond to the same portion of terrain illuminated by the radar.
- the values on which the process operates are the amplitude values of the signal that is acquired by the SAR relating to a certain cell of resolution pixels on the ground, namely the N amplitude values recorded at a certain pixel in the N acquisitions available on the interest area.
- Operating only on the amplitude values enables a smaller computational calculation to be made and enables the process to be made independent of a whole series of effects, which are well known in SAR interferometry, which alter the phase value of the signal, but which maintain the module thereof substantially unaltered, such as, for example, the atmospheric effects or the trajectory variations of the satellite.
- the process can also be applied to the intensity values of the images, i.e. the squared amplitude values.
- the process comprises selecting a pixel as a sample pixel.
- a vector of N amplitude values is constructed relating to the area illuminated by the radar during the course of the acquisitions.
- Vectors of amplitude values with N dimensions are thus obtained.
- the vector of amplitude of the sample pixel is defined as the sample vector.
- the process according to the invention comprises (figure 2), for each sample pixel 1 (indicated by a black square), the definition of an area of interest 10, said estimation window, within which to seek the pixels that are statistically homogeneous to the sample pixel.
- the shape and dimension of the estimation window are parameters that may vary from one application to another but usually a rectangular window is selected that comprises a few hundred pixels.
- the vector of N amplitude values is calculated in the same manner as the sample vector.
- the estimation window identifies a set of pixels and a set of vectors of amplitude that have the same dimension N as the sample vector and which can be compared with the sample vector in the search for a statistically homogenous behaviour.
- the problem is traceable to the comparison between two vectors of random variables containing the same number N of samples, about which it is desired to ascertain whether they can be considered to be two embodiments of the same distribution function or embodiments of different distribution functions. It is necessary to conduct a test to compare each vector associated with the pixel of the estimation window 10 and the sample vector.
- test can be of the non-parametric type.
- pixels 2 and 3 (figure 2) will be marked as being homogenous with the sample pixel 1 (where the pixels 3 are indicated by grey circles and the pixels 2 are indicated by black circles) and the pixels 4 will be marked as not being homogenous with the sample pixel (indicated by white circles).
- the process according to the invention thus comprises the following steps: acquiring a plurality of N radar images Al..AN by means of a SAR on the same area with similar acquisition geometries and such as to consent the common grid resampling of data, downstream of the resampling, selecting a pixel and identifying the pixel as a sample pixel 1, calculating a vector of N amplitude values relating to the sample pixel in the N images available and identifying the vector as a sample vector, defining an estimation window 10 (figure 2) for the sample pixel, for identifying a set of pixels in the neighbourhood of the sample pixel, calculating the vectors of N amplitude values for each other pixel contained in the estimation window, similarly to what is done for the sample pixel; - comparing, via a statistical test, each vector of amplitude associated with the pixels belonging to the estimation window with the sample vector to ascertain which vectors of amplitude are generated by the distribution function of the sample vector, identifying as pixels (figure 2) that are homogenous with the sample
- the set of the pixels that are statistically homogeneous with the sample pixel can be used to conduct estimates of averages or other estimates.
- Each point of the image can be selected as a sample pixel and the process can thus be conducted on all the pixels of the area of interest.
- Some sample pixels may not have homogeneous pixels contained in the respective estimation windows. This latter case is typical of so-called punctiform pixels, with electromagnetic behaviour that is distinctively different from that of the surrounding terrain.
- the process for identifying statistically homogeneous pixels of SAR images according to the invention is implemented by application software installed in a memory of a processing device; the latter comprises a microprocessor that converses with the memory to run said application software.
- the estimation window is chosen by the user.
- the process according to the invention is used to reduce the speckle noise of a SAR image.
- a dataset consisting of 75 multitemporal radar data acquired by the ERS-I and ERS-2 satellites of the European Space Agency is used to create, for each pixel of the image, the set of pixels that are statistically homogenous with the process according to the invention.
- the estimation window used in this and in the subsequent examples is rectangular in shape and measures 13 x 25 pixels (respectively in the range and the azimuth directions): it can thus contain a maximum value of 325 homogenous pixels.
- the map of the average reflectivity of the area of interest is shown, i.e. the average of the amplitude values relating to the 75 multitemporal radar data acquired by the ERS-I and ERS-2 satellites on Mount Etna; the horizontal dimension is the azimuth coordinate whilst the vertical dimension is the range coordinate.
- figure 4 for each pixel there is shown the number of statistically homogenous elements associated therewith identified by the process, whilst figure 5b shows the benefits of the use of the process according to the invention as an anti-speckle filter compared with an anti- speckle filter of known type (figure 5a).
- the speckle noise corresponds to variations in the amplitude values of the signal that are also located on homogenous targets that are observed in the data obtained by the so-called consistent observation systems, such as the SAR systems.
- the speckle noise is reduced by means of a simple algorithm that is movable only on the amplitude values relating to the pixels that are statistically homogeneous with the current pixel.
- the result of the filtering conducted according to the process according to the invention is notable, such as to be able to compare the single acquisition (filtered on adaptive windows corresponding to the statistically homogeneous pixels) with the map of average reflectivity (figure 3), in which all 75 available images are used.
- This whilst the spatial resolution of the data is maintained unaltered, does not, however make a multitemporal analysis of the reflectivity values possible but can, on the other hand, show variations over time of the RCS values originated by various phenomena such as changes in ground humidity, variations in vegetation, presence of artificial targets, etc.
- the second application of the process according to the invention is the use thereof as a filtering tool of SAR inter fero grams.
- FIGS. 6a and 6b By using the same database of SAR images acquired by the ERS satellites of the preceding example, in figures 6a and 6b there are shown the interferometric fringes of the zone of Valle del Bove, obtained by a pair of ERS images. Each 'fringe' corresponds to a motion of the terrain along the view line of a little less than 3 cm.
- the third application of the process according to the invention relates to the estimation of consistency of two SAR images.
- Figures 7a and 7b are compared with what is obtainable with the adaptive process disclosed in the preceding paragraphs and the result of traditional consistency estimation, i.e. by means of a process of a movable average on a 13x25 samples window, i.e. of the same dimensions as the estimation window of the homogenous pixels used in the examples but without any selection of the homogenous pixels; one definition of consistency is disclosed in the article by Touzi, Lopes, Bruniquel, Vachon, "Coherence estimation for SAR imagery" IEEE, Trans. Geosc. Remote Sensing, vol. 37, No. 1, pages 135-149, January 1999.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Radar Systems Or Details Thereof (AREA)
- Holo Graphy (AREA)
- Testing Of Coins (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
- Image Processing (AREA)
Abstract
Description
Claims
Priority Applications (7)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DK10713323.3T DK2415017T3 (en) | 2009-04-03 | 2010-03-26 | Process for identifying statistically homogeneous pixels in SAR images obtained in the same area |
EP10713323.3A EP2415017B1 (en) | 2009-04-03 | 2010-03-26 | Process for identifying statistically homogeneous pixels in sar images acquired on the same area |
JP2012502602A JP5587972B2 (en) | 2009-04-03 | 2010-03-26 | Method for identifying statistically homogeneous pixels in a SAR image acquired on the same region |
ES10713323T ES2701003T3 (en) | 2009-04-03 | 2010-03-26 | Process to identify statistically homogeneous pixels in SAR images acquired in the same area |
AU2010230359A AU2010230359B2 (en) | 2009-04-03 | 2010-03-26 | Process for identifying statistically homogeneous pixels in SAR images acquired on the same area |
US13/259,363 US8587471B2 (en) | 2009-04-03 | 2010-03-26 | Process for identifying statistically homogeneous pixels in SAR images acquired on the same area |
CA2756843A CA2756843A1 (en) | 2009-04-03 | 2010-03-26 | Process for identifying statistically homogeneous pixels in sar images acquired on the same area |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
ITMI2009A000535 | 2009-04-03 | ||
ITMI2009A000535A IT1393687B1 (en) | 2009-04-03 | 2009-04-03 | PROCEDURE FOR THE IDENTIFICATION OF PIXELS STATISTICALLY HOMOGENEOUS IN IMAGES ARE PURCHASED ON THE SAME AREA. |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2010112426A1 true WO2010112426A1 (en) | 2010-10-07 |
Family
ID=41213312
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/EP2010/054016 WO2010112426A1 (en) | 2009-04-03 | 2010-03-26 | Process for identifying statistically homogeneous pixels in sar images acquired on the same area |
Country Status (9)
Country | Link |
---|---|
US (1) | US8587471B2 (en) |
EP (1) | EP2415017B1 (en) |
JP (1) | JP5587972B2 (en) |
AU (1) | AU2010230359B2 (en) |
CA (1) | CA2756843A1 (en) |
DK (1) | DK2415017T3 (en) |
ES (1) | ES2701003T3 (en) |
IT (1) | IT1393687B1 (en) |
WO (1) | WO2010112426A1 (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103714542A (en) * | 2013-12-25 | 2014-04-09 | 中国人民解放军海军工程大学 | Extraction method for target highlight in low-resolution high-frequency sonar image |
CN116503651A (en) * | 2023-04-25 | 2023-07-28 | 北京东方至远科技股份有限公司 | Polarized SAR crop classification and identification system and method |
US11846702B2 (en) | 2019-07-18 | 2023-12-19 | Nec Corporation | Image processing device and image processing method |
Families Citing this family (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IT1393687B1 (en) * | 2009-04-03 | 2012-05-08 | Tele Rilevamento Europa T R E S R L | PROCEDURE FOR THE IDENTIFICATION OF PIXELS STATISTICALLY HOMOGENEOUS IN IMAGES ARE PURCHASED ON THE SAME AREA. |
IT1394733B1 (en) * | 2009-07-08 | 2012-07-13 | Milano Politecnico | PROCEDURE FOR FILTERING INTERFEROGRAMS GENERATED BY IMAGES ACQUIRED ON THE SAME AREA. |
JP5987114B2 (en) | 2013-07-12 | 2016-09-07 | 三菱電機株式会社 | High resolution image generation apparatus, high resolution image generation method, and high resolution image generation program |
CN103400137B (en) * | 2013-08-23 | 2016-06-22 | 中国科学院遥感与数字地球研究所 | A kind of building geometric parameter extracting method of SAR image |
US9791552B1 (en) | 2014-11-19 | 2017-10-17 | Src, Inc. | On-site calibration of array antenna systems |
US10102642B2 (en) * | 2015-11-25 | 2018-10-16 | Omni Ai, Inc. | Image driver that samples high-resolution image data |
US10317520B2 (en) | 2016-03-18 | 2019-06-11 | Src, Inc. | Radar system |
WO2018123748A1 (en) * | 2016-12-27 | 2018-07-05 | 日本電気株式会社 | Image analysis device, image analysis method, and computer-readable recording medium |
CN106651884B (en) * | 2016-12-30 | 2019-10-08 | 西安电子科技大学 | Mean field variation Bayes's SAR image segmentation method based on sketch structure |
US11391819B2 (en) * | 2018-07-18 | 2022-07-19 | Qualcomm Incorporate | Object verification using radar images |
CN110031816B (en) * | 2019-03-22 | 2021-04-27 | 中国民航科学技术研究院 | Airport flight area non-cooperative target classification and identification method based on bird detection radar |
CN111239734B (en) * | 2020-02-24 | 2022-09-13 | 西南交通大学 | Extraction method suitable for deep loess stable surface scatterers |
CN115267781B (en) * | 2022-09-28 | 2022-12-16 | 中山大学 | InSAR coherence estimation method based on multi-view SAR data set |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1387317A1 (en) * | 2001-04-19 | 2004-02-04 | Kabushiki Kaisha Toshiba | Image processing method and image processing device |
Family Cites Families (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4219811A (en) * | 1975-02-07 | 1980-08-26 | Hughes Aircraft Company | Synthetic array autofocus system |
JPS61100680A (en) * | 1984-10-23 | 1986-05-19 | Nec Corp | Synthetic aperture radar apparatus |
GB8714746D0 (en) * | 1987-06-24 | 1987-07-29 | Secr Defence | Synthetic aperture radar |
US4851848A (en) * | 1988-02-01 | 1989-07-25 | The United States Of America As Represented By The Secretary Of The Navy | Frequency agile synthetic aperture radar |
US5061931A (en) * | 1990-07-23 | 1991-10-29 | Selenia Industrie Elettroniche Associate S.P.A. | Recursive system for image forming by means of a spotlight synthetic aperture radar |
JP3503384B2 (en) * | 1997-01-14 | 2004-03-02 | 三菱電機株式会社 | Earth shape measurement device |
US6137437A (en) * | 1999-03-24 | 2000-10-24 | Agence Spatiale Europeenne | Spaceborne scatterometer |
JP4907798B2 (en) * | 2001-08-24 | 2012-04-04 | 株式会社東芝 | Ultrasonic diagnostic equipment |
US6563451B1 (en) * | 2002-01-16 | 2003-05-13 | Raytheon Company | Radar imaging system and method |
US7109911B1 (en) * | 2002-04-01 | 2006-09-19 | Cataldo Thomas J | Dual synthetic aperture radar system |
US7200243B2 (en) * | 2002-06-28 | 2007-04-03 | The United States Of America As Represented By The Secretary Of The Army | Spectral mixture process conditioned by spatially-smooth partitioning |
US6603424B1 (en) * | 2002-07-31 | 2003-08-05 | The Boeing Company | System, method and computer program product for reducing errors in synthetic aperture radar signals |
JP2005114589A (en) * | 2003-10-09 | 2005-04-28 | Mitsubishi Electric Corp | Radar image processing system |
US7298922B1 (en) * | 2004-07-07 | 2007-11-20 | Lockheed Martin Corporation | Synthetic panchromatic imagery method and system |
CN100517374C (en) * | 2005-12-29 | 2009-07-22 | 佳能株式会社 | Device and method for extracting text from document image having complex background |
US8116585B2 (en) * | 2007-08-09 | 2012-02-14 | Xerox Corporation | Background noise detection on rendered documents |
IT1393687B1 (en) * | 2009-04-03 | 2012-05-08 | Tele Rilevamento Europa T R E S R L | PROCEDURE FOR THE IDENTIFICATION OF PIXELS STATISTICALLY HOMOGENEOUS IN IMAGES ARE PURCHASED ON THE SAME AREA. |
-
2009
- 2009-04-03 IT ITMI2009A000535A patent/IT1393687B1/en active
-
2010
- 2010-03-26 ES ES10713323T patent/ES2701003T3/en active Active
- 2010-03-26 EP EP10713323.3A patent/EP2415017B1/en active Active
- 2010-03-26 CA CA2756843A patent/CA2756843A1/en not_active Abandoned
- 2010-03-26 WO PCT/EP2010/054016 patent/WO2010112426A1/en active Application Filing
- 2010-03-26 DK DK10713323.3T patent/DK2415017T3/en active
- 2010-03-26 US US13/259,363 patent/US8587471B2/en active Active
- 2010-03-26 JP JP2012502602A patent/JP5587972B2/en active Active
- 2010-03-26 AU AU2010230359A patent/AU2010230359B2/en active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1387317A1 (en) * | 2001-04-19 | 2004-02-04 | Kabushiki Kaisha Toshiba | Image processing method and image processing device |
Non-Patent Citations (7)
Title |
---|
"Numerical recipes in C: the art of scientific computing", 1988, UNIVERSITY OF CAMBRIDGE, pages: 620 - 628 |
CIUC M ET AL: "Adaptive-neighborhood speckle removal in multitemporal synthetic aperture radar images", APPLIED OPTICS, OSA, OPTICAL SOCIETY OF AMERICA, WASHINGTON, DC, vol. 40, no. 32, 10 November 2001 (2001-11-10), pages 5954 - 5966, XP002256732, ISSN: 0003-6935 * |
JONG-SEN LEE: "Digital Image Smoothing and the Sigma Filter", COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING, ELSEVIER SCIENCE, vol. 24, 1 January 1983 (1983-01-01), pages 255 - 269, XP002511283, ISSN: 0734-189X, [retrieved on 20090120] * |
MASSEY, F. J.: "The Kolmogorov-Smirnov Test for Goodness of Fit.", JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, vol. 46, no. 253, 1951, pages 68 - 78 |
MEDEIROS F N S ET AL: "Combined use of MAP estimation and K-means classifier for speckle noise filtering in SAR images", IMAGE ANALYSIS AND INTERPRETATION, 1998 IEEE SOUTHWEST SYMPOSIUM ON TUCSON, AZ, USA 5-7 APRIL 1998, NEW YORK, NY, USA,IEEE, US, 5 April 1998 (1998-04-05), pages 250 - 255, XP010274978, ISBN: 978-0-7803-4876-9 * |
S. MARCHAND-MAILLET; Y.M. SHARAIHA: "Binary digital image processing", 2000, ACADEMIC PRESS |
TOUZI; LOPES; BRUNIQUEL; VACHON: "Coherence estimation for SAR imagery", IEEE, TRANS. GEOSC. REMOTE SENSING, vol. 37, no. 1, January 1999 (1999-01-01), pages 135 - 149, XP011021198 |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103714542A (en) * | 2013-12-25 | 2014-04-09 | 中国人民解放军海军工程大学 | Extraction method for target highlight in low-resolution high-frequency sonar image |
US11846702B2 (en) | 2019-07-18 | 2023-12-19 | Nec Corporation | Image processing device and image processing method |
CN116503651A (en) * | 2023-04-25 | 2023-07-28 | 北京东方至远科技股份有限公司 | Polarized SAR crop classification and identification system and method |
CN116503651B (en) * | 2023-04-25 | 2023-11-14 | 北京东方至远科技股份有限公司 | Polarized SAR crop classification and identification system and method |
Also Published As
Publication number | Publication date |
---|---|
EP2415017A1 (en) | 2012-02-08 |
JP5587972B2 (en) | 2014-09-10 |
JP2012523030A (en) | 2012-09-27 |
US20120013501A1 (en) | 2012-01-19 |
ES2701003T3 (en) | 2019-02-20 |
AU2010230359B2 (en) | 2016-03-17 |
AU2010230359A1 (en) | 2011-11-03 |
US8587471B2 (en) | 2013-11-19 |
DK2415017T3 (en) | 2019-01-14 |
ITMI20090535A1 (en) | 2010-10-04 |
CA2756843A1 (en) | 2010-10-07 |
EP2415017B1 (en) | 2018-09-19 |
IT1393687B1 (en) | 2012-05-08 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8587471B2 (en) | Process for identifying statistically homogeneous pixels in SAR images acquired on the same area | |
JP5932643B2 (en) | Method for filtering interferogram obtained from SAR image acquired on same region | |
US10186015B1 (en) | Method and apparatus for enhancing 3D model resolution | |
US10042048B1 (en) | Superpixels for improved structure and terrain classification using multiple synthetic aperture radar image products | |
d'Alessandro et al. | Phenomenology of ground scattering in a tropical forest through polarimetric synthetic aperture radar tomography | |
Ertin et al. | Interferometric methods for three-dimensional target reconstruction with multipass circular SAR | |
US10325349B2 (en) | Method and apparatus for enhancing 3D model resolution | |
CN109752715B (en) | SAR data total-dispersion body detection method and device | |
Baier et al. | Nonlocal InSAR filtering for DEM generation and addressing the staircasing effect | |
Volosyuk et al. | Phenomenological description of coherent radar images based on the concepts of the measure of set and stochastic integral | |
US10535119B2 (en) | Method and apparatus for enhancing 3D model resolution | |
Refice et al. | On the use of anisotropic covariance models in estimating atmospheric DInSAR contributions | |
CN106897985B (en) | A kind of multi-angle SAR image fusion method based on visibility classification | |
Karkee et al. | Fusion of Optical and InSAR DEMs: Improving the Quality of Free Data. | |
Varekamp et al. | Segmentation of high-resolution InSAR data of a tropical forest using Fourier parameterized deformable models | |
Capaldo et al. | Radargrammetric Digital Surface Models Generation from TerraSAR-X Imagery: case studies, problems and potentialities | |
Pepin | Three-dimensional spherical SAR template and feature recognition | |
De Zan et al. | Multibaseline interferometry for natural scatterer characterization | |
e Geomatica–DICEA | RADARGRAMMETRIC DIGITAL SURFACE MODELS GENERATION FROM TERRASAR-X IMAGERY: CASE STUDIES, PROBLEMS AND POTENTIALITIES | |
Mariotti d’Alessandro et al. | Phenomenology Of Scattering Mechanisms In A Tropical Forest Through Polarimetric SAR Tomography3 | |
Acito et al. | Unsupervised change detection in very high spatial resolution COSMO-SkyMed SAR Images | |
Jiang | InSAR coherence estimation and applications to earth observation | |
Boyer et al. | Perceptual organization in range data: robust detection of low order surfaces in heavy clutter | |
Denney et al. | Some results from scattering-based tomography for HRR and SAR prediction | |
Thyagarajan | DEM Processing Methods |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 10713323 Country of ref document: EP Kind code of ref document: A1 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2012502602 Country of ref document: JP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 13259363 Country of ref document: US |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2756843 Country of ref document: CA |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2010713323 Country of ref document: EP |
|
ENP | Entry into the national phase |
Ref document number: 2010230359 Country of ref document: AU Date of ref document: 20100326 Kind code of ref document: A |