Anne H Schistad Solberg

• Andreassen, Børge Solli; Volgyes, David; Samset, Eigil & Solberg, Anne H Schistad (2022). Mitral Annulus Segmentation and Anatomical Orientation Detection in TEE Images Using Periodic 3D CNN. IEEE Access. ISSN 2169-3536. 10, s. 51472–51486. doi: 10.1109/ACCESS.2022.3174059.
• Nozari Mirarkolaei, Hani; Snare, Sten Roar & Solberg, Anne H Schistad (2021). A robust bidirectional motion-compensated interpolation algorithm to enhance temporal resolution of 3D echocardiography. Biomedical Signal Processing and Control. ISSN 1746-8094. 65, s. 1–11. doi: 10.1016/j.bspc.2020.102384.
• Mirarkolaei, Hani Nozari; Snare, Sten Roar; Solberg, Anne H Schistad & Steen, Erik Normann (2020). Frame rate up-conversion in cardiac ultrasound. Biomedical Signal Processing and Control. ISSN 1746-8094. 58. doi: 10.1016/j.bspc.2020.101863.
• Jahren, Tollef Struksnes; Steen, Erik Normann; Aase, Svein Arne & Solberg, Anne H Schistad (2020). Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. ISSN 0885-3010. 67(12), s. 2605–2614. doi: 10.1109/TUFFC.2020.2995118.
• Andreassen, Børge Solli; Veronesi, Federico; Gerard, Olivier; Solberg, Anne H Schistad & Samset, Eigil (2020). Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography. IEEE journal of biomedical and health informatics. ISSN 2168-2194. 24(4), s. 994–1003. doi: 10.1109/JBHI.2019.2959430. Fulltekst i vitenarkiv
• Waldeland, Anders U.; Coimbra, T.A.; Faccipieri, J. H.; Solberg, Anne H Schistad & Gelius, Leiv-J. (2019). Fast estimation of prestack Common Reflection Surface parameters. Geophysical Prospecting. ISSN 0016-8025. 67(5), s. 1163–1183. doi: 10.1111/1365-2478.12740.
• Kvam, Johannes; Gangsei, Lars Erik; Kongsro, Jørgen & Solberg, Anne H Schistad (2018). The use of deep learning to automate the segmentation of the skeleton from CT volumes of pigs. Translational Animal Science. ISSN 2573-2102. 2(3), s. 324–335. doi: 10.1093/tas/txy060.
• Waldeland, Anders Ueland; Zhao, Hao; Faccipieri, J. H.; Solberg, Anne H Schistad & Gelius, Leiv-J. (2018). Fast and robust common-reflection-surface parameter estimation. Geophysics. ISSN 0016-8033. 83(1), s. O1–O13. doi: 10.1190/GEO2017-0113.1. Fulltekst i vitenarkiv Vis sammendrag

  The common-reflection-surface (CRS) method offers a stack with higher signal-to-noise ratio at the cost of a time-consuming semblance search to obtain the stacking parameters. We have developed a fast method for extracting the CRS parameters using local slope and curvature. We estimate the slope and curvature with the gradient structure tensor and quadratic structure tensor on stacked data. This is done under the assumption that a stacking velocity is already available. Our method was compared with an existing slope-based method, in which the slope is extracted from prestack data. An experiment on synthetic data shows that our method has increased robustness against noise compared with the existing method. When applied to two real data sets, our method achieves accuracy comparable with the pragmatic and full semblance searches. Our method has the advantage of being approximately two and four orders of magnitude faster than the semblance searches.

• Waldeland, Anders Ueland; Jensen, Are Charles; Gelius, Leiv-J. & Solberg, Anne H Schistad (2018). Convolutional neural networks for automated seismic interpretation. The Leading Edge. ISSN 1070-485X. 37(7), s. 529–537. doi: 10.1190/tle37070529.1. Vis sammendrag

  Deep-learning methods have proved successful recently for solving problems in image analysis and natural language processing. One of these methods, convolutional neural networks (CNNs), is revolutionizing the field of image analysis and pushing the state of the art. CNNs consist of layers of convolutions with trainable filters. The input to the network is the raw image or seismic amplitudes, removing the need for feature/attribute engineering. During the training phase, the filter coefficients are found by iterative optimization. The network thereby learns how to compute good attributes to solve the given classification task. However, CNNs require large amounts of training data and must be carefully designed and trained to perform well. We look into the intuition behind this method and discuss considerations that must be made in order to make the method reliable. In particular, we discuss how deep learning can be used for automated seismic interpretation. As an example, we show how a CNN can be used for automatic interpretation of salt bodies.

• Breivik, Lars Hofsøy; Snare, Sten Roar; Steen, Erik Normann & Solberg, Anne H Schistad (2017). Real-time nonlocal means-based despeckling. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. ISSN 0885-3010. 64(6), s. 959–977. doi: 10.1109/TUFFC.2017.2686326.
• Landmark, Knut & Solberg, Anne H Schistad (2015). Reducing scalloping in synthetic aperture radar images using a composite image transform. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. 9643(Image and Signal Processing for Remote Sensing XXI). doi: 10.1117/12.2194952.
• Landmark, Knut; Solberg, Anne H Schistad; Albregtsen, Fritz; Austeng, Andreas & Hansen, Roy Edgar (2015). A Radon-Transform-Based Image Noise Filter - With Applications to Multibeam Bathymetry. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 53(11), s. 6252–6273. doi: 10.1109/TGRS.2015.2436380.
• Breivik, Lars Hofsøy; Snare, Sten Roar; Mirarkolaei, Hani Nozari; Steen, Erik Normann & Solberg, Anne H Schistad (2014). Multiscale nonlocal means method for ultrasound despeckling. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. s. 1324–1327. doi: 10.1109/ULTSYM.2014.0327.
• Mirarkolaei, Hani Nozari; Snare, Sten Roar; Breivik, Lars Hofsøy; Steen, Erik Normann & Solberg, Anne H Schistad (2014). Fixed Point Motion Estimation for frame rate up conversion in cardiac ultrasound. Proceedings - IEEE Ultrasonics Symposium. ISSN 1948-5719. s. 2351–2354. doi: 10.1109/ULTSYM.2014.0586.
• Breivik, Lars Hofsøy; Snare, Sten Roar; Mirarkolaei, Hani Nozari; Steen, Erik & Solberg, Anne H Schistad (2014). Multiscale nonlocal means method for ultrasound despeckling, 2014 IEEE International Ultrasonics Symposium Proceedings (IUS), Chicago, Illinois USA, September 3-6, 2014. IEEE conference proceedings. ISSN 978-1-4799-7049-0. s. 1324–1327. doi: 10.1109/ULTSYM.2014.0327.
• Mirarkolaei, Hani Nozari; Snare, Sten Roar; Breivik, Lars Hofsøy; Steen, Erik & Solberg, Anne H Schistad (2014). Fixed Point Motion Estimation for frame rate up conversion in cardiac ultrasound, 2014 IEEE International Ultrasonics Symposium Proceedings (IUS), Chicago, Illinois USA, September 3-6, 2014. IEEE conference proceedings. ISSN 978-1-4799-7049-0. s. 2351–2354. doi: 10.1109/ULTSYM.2014.0586.
• Salberg, Arnt Børre; Rudjord, Øystein & Solberg, Anne H Schistad (2014). Oil spill detection in hybrid-polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 52(10), s. 6521–6533. doi: 10.1109/TGRS.2013.2297193. Vis sammendrag

  Oil spill detection in SAR images operating in a hybrid-polarimetric mode is examined. We propose and review several strategies for oil spill detection in hybrid-polarimetric SAR data. The retrieved measures are successfully applied to SAR data covering oil spill experiments outside Norway and the Deepwater Horizon incident in the Gulf of Mexico. It is shown that, under the assumption of a two-scale Bragg scattering model, a coherence measure may be recovered equally well from hybrid-polarimetric data, as for full-polarimetric data, and that this measure may be retrieved directly from the measurements without the need for any additional assumptions. The results show that low-wind lookalikes may be suppressed at the same time as the contrast of the oil spills is maintained using hybrid-polarimetric data and that multifeature images may be constructed to further enhance the oil spill detection performance. Due to the potential of wide swath widths, we conclude that hybrid-polarity is an attractive mode for future SAR-based oil spill monitoring.

• Landmark, Knut; Solberg, Anne H Schistad; Austeng, Andreas & Hansen, Roy Edgar (2014). Bayesian seabed classification using angle-dependent backscatter data from multibeam echo sounders. IEEE Journal of Oceanic Engineering. ISSN 0364-9059. 39(4), s. 724–739. doi: 10.1109/JOE.2013.2281133.
• Berthelot, Angélique; Solberg, Anne H Schistad & Gelius, Leiv-J. (2013). Texture attributes for detection of salt. Journal of Applied Geophysics. ISSN 0926-9851. 88, s. 52–69. doi: 10.1016/j.jappgeo.2012.09.006.
• Solberg, Anne H Schistad (2013). Deriving structural information from robust phase estimates. Society of Exploration Geophysicists. Expanded Abstracts with Biographies. ISSN 1052-3812. doi: 10.1190/segam2013-0985.1.
• Landmark, Knut; Solberg, Anne H Schistad; Nilsen, Carl-Inge Colombo; Austeng, Andreas & Hansen, Roy Edgar (2012). Bayesian Seabed Classification Using Angle-Dependent Backscatter Data from Multibeam Sonar, 11th European Conference on Underwater Acoustics 2012 (ECUA 2012). Curran Associates, Inc.. ISSN 978-1-62276-192-0. s. 417–424. doi: 10.1109/joe.2013.2281133.
• Solberg, Anne H Schistad (2012). Remote Sensing of Ocean Oil-Spill Pollution. Proceedings of the IEEE. ISSN 0018-9219. 100(10), s. 2931–2945. doi: 10.1109/JPROC.2012.2196250. Vis sammendrag

  Oil spills on the sea surface are observed relatively often. Pollution due to either accidents or deliberate oily discharges from ships represents a serious threat to the marine environment. Operational oil spill monitoring is currently done using a combination of satellite monitoring and aircraft surveillance. The combined use of satellite-based synthetic aperture radar (SAR) images and aircraft surveillance flights is a cost-effective way to monitor oil spills in large ocean areas and catch the polluters. SAR images enable covering large areas, but aircraft observations are needed to prosecute the polluter, and in certain cases to verity the oil spill. Traditionally, oil spill detection is based on single polarization SAR images. Oil spills can be discriminated from look-alikes based on a set of features describing the contrast, shape, homogeneity, source, and surroundings of the slick. Good performance is reported for single-polarization oil spill detection, but in certain cases the oil slicks cannot be discriminated from biogenic films. In the recent years, a number of studies have shown that polarimetric SAR can improve the discrimination between oil slicks and biogenic films. Several features computed from dual-pol or quad-pol images have been proposed. These include both quad-pol features like polarimetric entropy and anisotropy, mean scattering angle, polarimetric span, conformity coefficient, as well as the dual-pol features standard deviation of the copolarized phase difference and the copolarized correlation coefficient. As dual-pol SAR imagery is now available on a regular basis from Cosmo Skymed and TerraSAR-X, and quad-pol data are available from RADARSAT-2, polarimetric SAR can now be utilized on a more regular basis. Optical data from sensors like Aqua MODIS and ENVISAT MERIS can be a useful supplement under certain cloud-free conditions.

• Salberg, Arnt Børre; Rudjord, Øystein & Solberg, Anne H Schistad (2012). Model based oil spill detection using polarimetric SAR, Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. IEEE Press. ISSN 978-1-4673-1160-1. s. 5884–5887. doi: 10.1109/IGARSS.2012.6352270.
• Solberg, Anne H Schistad; Jensen, Are Charles & Gelius, Leiv-J. (2012). Robust 2D Hilbert Atributes of Local Amplitude and Phase. I EAGE, 2012 (Red.), 74th EAGE Conference & Exhibition incorporating SPE EUROPEC 2012. European Association of Geoscientists and Engineers (EAGE). ISSN 978-90-73834-27-9.
• Salberg, Arnt Børre; Rudjord, Øystein & Solberg, Anne H Schistad (2012). Model based oil spill detection using polarimetric SAR. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. s. 5884–5887. doi: 10.1109/IGARSS.2012.6352270.
• Solberg, Anne H S; Berthelot, Angélique & Gelius, Leiv-J. (2011). Gabor Filters for Segmentation of Salt Structures, Extended Abstracts DVD of the 73rd EAGE Conference & Exhibition incorporating SPE EUROPEC. European Association of Geoscientists and Engineers (EAGE). ISSN 978-90-73834-12-5.
• Solberg, Anne H S & Gelius, Leiv-J. (2011). New texture attributes from local 2D Fourier spectra. Extended Abstract. I Koster, Klaas (Red.), Society of Exploration Geophysicists International Exposition and 81st Annual Meeting 2011 (SEG San Antonio 2011). Curran Associates, Inc.. ISSN 9781618391841. s. 1165–1169. doi: 10.1190/1.3627409.
• Berthelot, Angélique; Solberg, Anne H S; Jarsve, Erlend Morisbak & Gelius, Leiv-J. (2011). Salt diapirs without well defined boundaries - a feasibility study of semi-automatic detection. Geophysical Prospecting. ISSN 0016-8025. 59, s. 682–696. doi: 10.1111/j.1365-2478.2011.00950.x.
• Berthelot, Angèlique; Solberg, Anne H S & Gelius, Leiv-J. (2010). Attributes designed for detection of salt diapirs without well defined boundaries. I EAGE, 2010 (Red.), 72nd EAGE Conference & Exhibition incorporating SPE EUROPEC 2010 Barcelona. European Association of Geoscientists and Engineers (EAGE). ISSN 978-90-73781-86-3.
• Jensen, Are Fritz Charles; Loog, Marco & Solberg, Anne H S (2010). Using Multiscale Spectra in Regularizing Covariance Matrices for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 48(4), s. 1851–1859. doi: 10.1109/TGRS.2009.2036842.
• Berthelot, Angelique; Gelius, Leiv-J. & Solberg, Anne H S (2009). Use of seismic attributes in auto-tracking of salt structures - A feasibility study. I Mogi, Toru (Red.), The 9th SEGJ International Symposium Imaging and Interpretation- Science and Technology for Sustainable Development -. Society of Exploration Geophysicists of Japan (SEGJ). ISSN 978-4-938493-06-6.
• Jensen, Are Fritz Charles; Berge, Asbjørn & Solberg, Anne H S (2008). Regression Approaches to Small Sample Inverse Covariance Matrix Estimation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 46(10), s. 2814–2822. doi: 10.1109/TGRS.2008.2001169.
• Brekke, Camilla & Solberg, Anne H S (2008). Classifiers and confidence estimation for oil spill detection in ENVISAT ASAR images. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. 5. doi: 10.1109/LGRS.2007.907174.
• Solberg, Anne H S; Berge, Asbjørn & Jensen, Are Fritz Charles (2007). Robust classification of hyperspectral images. I Bruzzone, Lorenzo (Red.), Image and Signal Processing for Remote Sensing XIII. SPIE - The International Society for Optics and Photonics. ISSN 9780819469069.
• Brekke, Camilla; Solberg, Anne H S & Storvik, Geir Olve (2007). Classifying Oil Spills and Look-Alikes in Envisat ASAR Images. I Lacoste, H & Ouwehand, L (Red.), Proceedings of the Envisat Symposium 2007 - Montreux, Switzerland. European Space Agency. ISSN 0000000000. Vis sammendrag

  Egentlig ISBN er 92-9291-200-1

• Jensen, Are Fritz Charles & Solberg, Anne H S (2007). Fast Hyperspectral Feature Reduction Using Piecewise Constant Function Approximations. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. 4(4), s. 547–551. doi: 10.1109/LGRS.2007.896331.
• Berge, Asbjørn; Jensen, Are Fritz Charles & Solberg, Anne H S (2007). Sparse inverse covariance estimates for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 45. doi: 10.1109/TGRS.2007.892598.
• Berge, Asbjørn & Solberg, Anne H S (2007). Improving hyperspectral classifiers: the difference between reducing data dimensionality and reducing classifier parameter complexity. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. s. 293–302.
• Solberg, Anne Helene Schistad; Brekke, C & Husøy, Per Ove (2007). Oil spill detection in Radarsat and Envisat SAR images. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 45. doi: 10.1109/TGRS.2006.887019. Vis sammendrag

  We present algorithms for automatic detection of oil spills in synthetic aperture radar (SAR) images. The algorithms consist of three main parts, namely: 1) detection of dark spots; 2) feature extraction from the dark spot candidates; and 3) classification of dark spots as oil spills or look-alikes. The algorithms have been trained on a large number of Radarsat and Envisat Advanced Synthetic Aperture Radar (ASAR) images. The performance of the algorithm is compared to manual and semiautomatic approaches in a benchmark study using 59 Radarsat and Envisat images. The algorithms can be considered to be a good alternative to manual inspection when large ocean areas are to be inspected

• Solberg, Anne H S (2006). Data fusion for remote sensing applications. I Chen, Chi (Red.), Signal and Image Processing for Remote Sensing. Taylor & Francis. ISSN 9780849350917. s. 515–537.
• Solberg, Anne H S & Brekke, Camilla (2006). Classification Methods for Oil Spill Detection in ENVISAT ASAR images. I Bruzzone, Lorenzo (Red.), Image and Signal Processing for Remote Sensing. SPIE - The International Society for Optics and Photonics. ISSN 0819464600.
• Berge, Asbjørn & Solberg, Anne H S (2006). Sparse covariance estimates for high dimensional classification using the Cholesky decomposition. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 4109, s. 835–843.
• Berge, Asbjørn & Solberg, Anne H S (2006). Structured Gaussian components for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 44, s. 3386–3396.
• Brekke, Camilla & Solberg, Anne H S (2005). Oil spill detection by satellite remote sensing. Remote Sensing of Environment. ISSN 0034-4257. 95(1), s. 1–13.
• Storvik, Geir Olve; Fjørtoft, Roger Sverre & Solberg, Anne H S (2005). A Bayesian approach to classification of multi-scale remote sensing data. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 43(3), s. 539–547. doi: 10.1109/TGRS.2004.841395. Vis sammendrag

  Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This work presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset. Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Results obtained on simulated and real satellite images are presented.

• Brekke, C & Solberg, Anne H S (2005). Feature extraction for oil spill detection based on SAR images. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 3540.
• Aurdal, Lars; Huseby, Ragnar Bang; Eikvil, Line; Solberg, Rune; Vikhamar Schuler, Dagrun & Solberg, Anne H Schistad (2005). Use of hidden Markov models and phenology for multitemporal satellite image classification: applications to mountain vegetation classification. I King, Roger L. & Younan, Nicolas H. (Red.), Proceedings of the 2005 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Biloxi, Mississippi, USA, 16-18 May 2005. IEEE conference proceedings. ISSN 0-7803-9118-7. s. 220–224. doi: 10.1109/AMTRSI.2005.1469877.
• Huseby, Ragnar Bang; Aurdal, Lars; Eikvil, Line; Solberg, Rune; Vikhamar Schuler, Dagrun & Solberg, Anne H Schistad (2005). Alignment of growth seasons from satellite data. I King, Roger L. & Younan, Nicolas H. (Red.), Proceedings of the 2005 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Biloxi, Mississippi, USA, 16-18 May 2005. IEEE conference proceedings. ISSN 0-7803-9118-7. s. 213–216. doi: 10.1109/AMTRSI.2005.1469875.
• Solberg, Anne H S (2004). Flexible nonlinear contextual classification. Pattern Recognition Letters. ISSN 0167-8655. 25(13), s. 1501–1508.
• Solberg, Anne H. Schistad (1999). Contextual data fusion applied to forest map revision. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 37(3), s. 1234–1243.
• Solberg, Anne H S; Storvik, Geir Olve; Solberg, Rune & Volden, Espen (1999). Automatic detection of oil spills in ERS SAR images. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892. 37(4), s. 1916–1924. doi: 10.1109/36.774704. Vis sammendrag

  We present algorithms for the automatic detection of oil spills in SAR images. The developed framework consists of first detecting dark spots in the image, then computing a set of features for each dark spot, before the spot is classified as either an oil slick or a ''lookalike'' (other oceanographic phenomena which resemble oil slicks), The classification rule is constructed by combining statistical modeling with a rule-based approach. Prior knowledge about the higher probability for the presence of oil slicks around ships and oil platforms is incorporated into the model, In addition, knowledge about the external conditions like mind level and slick surroundings are taken into account. The presented algorithms are tested on 84 SAR images. The algorithm can discriminate between oil slicks and lookalikes with high accuracy. 94% of the oil slicks and 99% of the lookalikes mere correctly classified.

• Holden, Marit; Solberg, Anne H. Schistad & Solberg, Rune (1998). Wet snow-cover mapping by C- and L-band polarimetric SAR. Ukjent. s. 1533–1537.
• Holden, Marit; Solberg, Anne H. Schistad & Solberg, Rune (1998). Wet snow-cover mapping by C- and L-band polarimtric SAR. Ukjent.

Se alle arbeider i Cristin

• Solberg, Anne H. Schistad (2006). Data fusion for remote sensing applications, in Signal and Image Processing for Remote Sensing (ed. C. H. Chen). Taylor and Francis Books. 24 s.

Se alle arbeider i Cristin

• Jenssen, Robert; Solberg, Anne H Schistad & Eikvil, Line (2021). Norge er verdensledende på bildeanalyse med kunstig intelligens.
• Di, Haibin; Zhao, Tao; Jayaram, Vikram; Wu, Xinming; Huang, Lei & AlRegib, Ghassan [Vis alle 15 forfattere av denne artikkelen] (2019). Introduction to special section: Machine learning in seismic data analysis. Interpretation. ISSN 2324-8858. 7(3), s. SEI–SEII. doi: 10.1190/INT-2019-0609-SPSEINTRO.1.
• Waldeland, Anders Ueland & Solberg, Anne H Schistad (2017). Salt Classification Using Deep Learning.
• Waldeland, Anders Ueland & Solberg, Anne H Schistad (2016). 3D Attributes and Classification of Salt Bodies on Unlabelled Datasets.
• Hverven, Stine Myhre; Austeng, Andreas & Solberg, Anne H Schistad (2016). Adaptive Beamforming on Muscle Fiber Features.
• Salberg, Arnt Børre; Rudjord, Øystein & Solberg, Anne H S (2012). Oil spill detection in compact polarimetry SAR images.
• Berthelot, Angélique; Solberg, Anne H Schistad; Morisbak, Erlend & Gelius, Leiv-J. (2012). 3D segmentation of salt using texture attributes.
• Solberg, Anne H Schistad; Gelius, Leiv-J.; Jensen, Are Charles & Heeremans, Michel (2012). Phase attributes by considering local 3D structure orientation.
• Jensen, Are Fritz Charles; Berge, Asbjørn & Solberg, Anne H S (2007). Regression Approaches to Small Sample Inverse Covariance Matrix Estimation for Hyperspectral Image Classification.
• Brekke, Camilla & Solberg, Anne H. Schistad (2006). Classification methods for oil spill detection by SAR imaging.
• Solberg, Anne H S & Brekke, Camilla (2006). Automatisk varsling av oljesøl. [Avis]. Apollon.
• Solberg, Anne H S (2006). Effektiv varsling av oljesøl. [Avis]. Havneavisen.
• Berge, Asbjørn & Solberg, Anne H S (2006). Sparse covariance estimates for hyperspectral image classification using the Cholesky decomposition.
• Brekke, Camilla & Solberg, Anne H S (2006). Classification Methods for Oil Spill Detection in ENVISAT ASAR Images.
• Jensen, Tine; Solberg, Anne & Ulvik, Oddbjørg Skjær (2005). Doing research with children in vulnerable situations: Methodological and ethical challenges.
• Brekke, Camilla & Solberg, Anne H S (2005). Feature extraction for oil spill detection based on SAR images.
• Solberg, Anne H S (2005). Automatic oil spill detection based on ENVISAT, RADARSAT and ERS images.
• Solberg, Anne H S (2004). Oljesøldeteksjon.
• Solberg, Anne H S; Brekke, Camilla; Solberg, Rune & Husøy, Per Ove (2004). Algorithms for oil spill detection in Radarsat and ENVISAT SAR images.
• Solberg, Anne H S (2004). Automatic detection of oil spills in ERS, Radarsat and Envisat images.
• Berge, Asbjørn & Solberg, Anne H S (2004). Robust Classification of Hyperspectral Data.
• Berge, Asbjørn & Solberg, Anne H S (2004). A Comparison of Methods for Improving Classification of Hyperspectral Data.
• Storvik, Geir Olve; Fjørtoft, Roger Sverre & Solberg, Anne H S (2003). Parameter estimation and classification of multi-scale remote sensing data.
• Solberg, Anne H S; Dokken, Sverre Thune & Solberg, Rune (2003). Automatic detection of oil spills in ENVISAT, Radarsat and ERS SAR images.
• Solberg, Anne H S & Storvik, Geir Olve (2003). Joint distributions for multitemporal series of radar images.
• Solberg, Anne H S (2002). Miljøovervåkning av havet - hvordan kan datamaskinen se oljesøl?
• Fjørtoft, Roger Sverre; Solberg, Anne H S & Storvik, Geir Olve (2002). A new approach to statistical multisensor image classification.
• Solberg, Anne H S (2002). Flexible non-linear contextual classification.
• Solberg, Anne H S; Storvik, Geir Olve & Fjørtoft, Roger Sverre (2002). A Comparison of Criteria for Decision Fusion and Parameter Estimation in Statistical Multisensor Image Classification.
• Solberg, Anne H S (2001). Nonlinear contextual classification - a comparative study. Vis sammendrag

  We develop a framework for nonlinear contextual classification by viewing classification as a regression problem and applying Markov random field theory for neighboring pixels in an image. We compare the effect of using a Gaussian classifier, neural nets, classification trees and recent statistical approaches to nonlinear classification. The effect of using different discriminant functions is compared to the effect of a contextual model for three different data sets from remote sensing applications. For all of the data sets, the use of a contextual model increases the classification accuracy. For the first data set, which fits a Gaussian model fairly well, there were no significant differences in classifier performance for the various discriminant functions. For the other two data sets, there were not very large differences between the performance of the various noncontextual classifiers. However, for the contextual model, some of the nonlinear classifiers performed significantly better than the Gaussian classifier.

• Rindal, Ole Marius Hoel; Austeng, Andreas & Solberg, Anne H Schistad (2019). Software Beamforming in Medical Ultrasound Imaging - a blessing and a curse. Universitet i Oslo. Fulltekst i vitenarkiv
• Waldeland, Anders U.; Solberg, Anne H Schistad & Gelius, Leiv-J. (2018). Seismic image analysis for applications related to iterative 3D velocity model building. Unipub UiO.
• Salberg, Arnt Børre; Longépé, Nicolas; Hansen, Morten Wergeland; Rudjord, Øystein; Larsen, Siri Øyen & Solberg, Anne H Schistad [Vis alle 8 forfattere av denne artikkelen] (2013). Report SAR analysis and comparisons - EU FP-7 project SeaU, Deliverable 12.1. Research Executive Agency - European Commission.
• Solberg, Anne H S & Trier, Øivind Thorvald Due (2011). Radarsat-2 oil spill detection. Performance study 2010. Norsk Regnesentral.
• Salberg, Arnt-Børre; Solberg, Svein; Solberg, Anne H. Schistad & Lange, Holger (2009). Leaf Area Index Estimation from Land Cover Classification Based on Hyperspectral Data. Norsk Regnesentral.
• Solberg, Anne H. Schistad & Trier, Øivind Thorvald Due (2009). SatHavOlje. Improvements during the 2008 project. Norsk Regnesentral.
• Solberg, Anne H. Schistad (2008). Confidence Estimation - Report for SatHav Olje. Norsk Regnesentral.
• Huseby, Ragnar Bang & Solberg, Anne H. Schistad (2008). Statistical analysis of oil spill data. Norsk Regnesentral.
• Storvik, Geir Olve; Fjørtoft, Roger & Solberg, Anne H S (2003). Parameter estimation and classification of multi-scale remote sensing data. Norsk Regnesentral.
• Storvik, Geir Olve; Fjørtoft, Roger & Solberg, Anne H S (2002). Integration of multisource image data at different resolutions and time points. A mathematical framework for EOtools. Norsk Regnesentral.
• Amlien, Jostein & Solberg, Anne H. Schistad (2001). Methods for retrieval of forest parameters from satellite remote sensing. Norsk Regnesentral.
• Eikvil, Line; Holden, Marit & Solberg, Anne H. Schistad (1999). Seismic Attribute Analysis, Part IV. Norsk Regnesentral.
• Solberg, Anne H. Schistad & Solberg, Rune (1999). SAR interferometry - A survey. ISSN 82-539-0421-5.
• Volden, Espen; Solberg, Anne H. Schistad; Huseby, Ragnar Bang & Solberg, Rune (1998). Forest mapping my airborne remote sensing techniques. ISSN 82-539-0448-7.
• Holden, Marit; Solberg, Anne H. Schistad & Solberg, Rune (1998). Snow mapping by multifrequency polarimetric SAR - An investigation of EMISAR data from the EMAC'95 snow campaign in Norway. ISSN 82-539-0417-7.
• Solberg, Anne H. Schistad; Holden, Marit; Storvik, Geir; Teigland, André & Møller-Holst, Johan (1998). Seismic image analysis: 3D texture attributes and classification - part III. Norsk Regnesentral.

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