Automatic matching of high‐resolution SAR images

Based on high‐resolution SAR data, in this paper, a novel automatic matching model is proposed. The model, which employs a coarse to fine strategy as a whole, consists of three steps. In the first step, edge features are extracted on different levels of pyramid images and an efficient Hausdorff distance‐based method is used to yield a coarse global feature match. Due to bi‐tree searching, the bottleneck of Hausdorff distance's matching is well resolved. Secondly, SSDA (Sequence Similarity Detection Algorithm) is employed to acquire tie‐points using a cross‐searching approach which treats features extracted from master and slave images equally. Finally, local‐adaptive splitting algorithm with MMSE (Minimum Mean Square Error) is used to achieve a fine matching; local‐adaptive splitting algorithm is the essential process to achieve sub‐pixel matching accuracy, which enhances the process's flexibility and robustness.

Airborne SAR images with high resolution are provided by the Institute of Electronics, CAS and used for experiments—the results of the experiments demonstrate that the model proposed in this paper is robust, with high accuracy (up to a fraction of a pixel), and can be successfully applied to automatic matching of high‐resolution SAR images.     

SAR‐based land cover classification of Kuwait

Orbital synthetic aperture radar (SAR) C‐band data acquired by ERS‐1/2 in vv‐polarization and Radarsat in hh‐polarization during the period from 1996 to 1999 were used to evaluate their combined information potential for classification of land cover in the arid environment of Kuwait. Individual SAR scenes were orthorectified using a digital elevation model (DEM) of Kuwait, radiometrically adjusted for incidence angle effects, and mosaics were generated for the whole country. The data were coregistered as multichannel composites and integrated with geographical information system (GIS) layers of roads, hydrology, soils and vegetation. An adaptive spatial filter was used to increase the number of effective independent looks prior to generation of feature vectors based on SAR backscatter power values. A total of 13 classes of the joint ERS‐1/2 and Radarsat images were identified based on Bhattacharya distance and geospatial pattern. The C‐band radar backscatter observed by ERS and Radarsat was found to be related to vegetation cover, surface roughness, percentage of coarse material in the surface layer and moisture conditions. These factors are not independent, but are known to be correlated. The complexity of these dependencies made unambiguous classification of surface material difficult when using C‐band data alone. Nevertheless, class labels were assigned using a maximum likelihood supervised classification incorporating field measurements and ancillary data such as soil, and surface sediment maps. When used in a simple two‐class classification (e.g. low vs. high vegetation cover fraction, or smooth vs. rough soils), the overall accuracy of the combined ERS and Radarsat data was between 70 and 80%. The generated dataset is amenable to several label definitions based on the requirements of the intended use.     

Single‐look classification accuracy for polarimetric SAR

Polarimetric Synthetic Aperture Radar (SAR) systems such as ALOS‐PALSAR and Radarsat‐2 can operate in many different modes. The use of additional polarizations may require additional time and operating power and it is important to justify this by increased classification accuracy. A fully polarimetric, dual frequency AIRSAR scene from a rice‐growing area in Japan was classified by a maximum likelihood method based on the Wishart distribution. It is shown how the measured covariance matrices determine the separation accuracy between two classes. Closed form expressions are then given for the expected single‐look accuracy of the maximum likelihood classifier as a function of the class covariance matrices. This can be used to quickly compare the high spatial resolution classification performance of different polarimetric systems to decide upon a particular operating mode.     

Assessment of stand‐wise stem volume retrieval in boreal forest from JERS‐1 L‐band SAR backscatter

JERS‐1 L‐band SAR backscatter from test sites in Sweden, Finland and Siberia has been investigated to determine the accuracy level achievable in the boreal zone for stand‐wise forest stem volume retrieval using a model‐based approach. The extensive ground‐data and SAR imagery datasets available allowed analysis of the backscatter temporal dynamics. In dense forests the backscatter primarily depended on the frozen/unfrozen state of the canopy, showing a ～4 dB difference. In sparse forests, the backscatter depended primarily on the dielectric properties of the forest floor, showing smaller differences throughout the year. Backscatter modelling as a function of stem volume was carried out by means of a simple L‐band Water Cloud related scattering model. At each test site, the model fitted the measurements used for training irrespective of the weather conditions. Of the three a priori unknown model parameters, the forest transmissivity coefficient was most affected by seasonal conditions and test site specific features (stand structure, forest management, etc.). Several factors determined the coefficient's estimate, namely weather conditions at acquisition, structural heterogeneities of the forest stands within a test site, forest management practice and ground data accuracy. Stem volume retrieval was strongly influenced by these factors. It performed best under unfrozen conditions and results were temporally consistent. Multi‐temporal combination of single‐image estimates eliminated outliers and slightly decreased the estimation error. Retrieved and measured stem volumes were in good agreement up to maximum levels in Sweden and Finland. For the intensively managed test site in Sweden a 25% relative rms error was obtained. Higher errors were achieved in the larger and more heterogeneous forest test sites in Siberia. Hence, L‐band backscatter can be considered a good candidate for stand‐wise stem volume retrieval in boreal forest, although the forest site conditions play a fundamental role for the final accuracy.     

Integration of multitemporal/polarization C‐band SAR data sets for land‐cover classification

This paper investigates the potential of multitemporal/polarization C‐band SAR data for land‐cover classification. Multitemporal Radarsat‐1 data with HH polarization and ENVISAT ASAR data with VV polarization acquired in the Yedang plain, Korea are used for the classification of typical five land‐cover classes in an agricultural area. The presented methodologies consist of two analytical stages: one for feature extraction and the other for classification based on the combination of features. Both a traditional SAR signal property analysis‐based approach and principal‐component analysis (PCA) are applied in the feature extraction stage. Special concerns are in the interpretation of each principal component by using principal‐component loading. The tau model applied as a decision‐level fusion methodology can provide a formal framework in which the posteriori probabilities derived from different sensor data can be combined. From the case study results, the combination of PCA‐based features showed improved classification accuracy for both Radarsat‐1 and ENVISAT ASAR data, as compared with the traditional SAR signal property analysis‐based approach. The integration of PCA‐based features based on multiple polarization (i.e. HH from Radarsat‐1, and both VV and VH from ENVISAT ASAR) and different incidence angles contributed to a significant improvement of discrimination capability for dry fields which could not be properly classified by using only Radarsat‐1 or ENVISAT ASAR data, and thus showed the best classification accuracy. The results of this case study indicate that the use of multiple polarization SAR data with a proper feature extraction stage would improve classification accuracy in multitemporal SAR data classification, although further consideration should be given to the polarization and incidence angle dependency of complex land‐cover classes through more experiments.     

Calibration and validation of a set of multi‐aspect airborne polarimetric Pi‐SAR data

To implement target reconstruction from multi‐aspect SAR data, a simple method of first calibrating multi‐aspect data is presented. It requires that at least one aspect is calibrated beforehand, and other aspects are then calibrated with respect to this calibrated aspect. A natural object, such as flat bare ground, is usually chosen as a reference target, and is expected to preserve identical scattering for all aspects. Thereafter, the channel imbalance factors are estimated from the distribution of the phase difference and amplitude ratio of co‐polarized, hh and vv, echoes of the reference target, and are then used to compensate the whole SAR images. This approach was applied to the calibration of four‐aspect airborne Pi‐SAR (Polarimetric and Interferometric Synthetic Aperture Radar) data for target reconstruction. Based on the same principle, another potential application is calibration of descending (ascending) data using the calibrated ascending (descending) data.     

Assessment of the ASAR sensor radiometric quality in comparison to ERS‐2 and RADARSAT‐1 SAR data

The estimation of geophysical parameters from Synthetic Aperture Radar (SAR) data necessitates well‐calibrated sensors with good radiometric precision. In this paper, the radiometric calibration of the new Advanced Synthetic Aperture Radar (ASAR‐ENVISAT) sensor was assessed by comparing ASAR data with ERS‐2 and RADARSAT‐1 SAR data. By analysing the difference between radar signals of forest stands, the results show differences of varying importance between the ASAR on the one hand, and the ERS‐2 and the RADARSAT‐1 on the other. For recent data acquired at the end of 2005, the difference varies from ?0.72 to +0.72 dB, with temporal variations that can reach 1.1 dB. For older data acquired in 2003 and 2004, we observe a sharp decrease in the radar signal in the range direction, which can attain 3.5 dB. The use of revised calibration constants provided recently by the European Space Agency (ESA) significantly improves the results of the radiometric calibration, where the difference between the ASAR and the other SARs will be lower than 0.5 dB.     

Biomass measurements and relationships with Landsat‐7/ETM+ and JERS‐1/SAR data over Canada's western sub‐arctic and low arctic

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10–100 t ha^?1 for woodlands, 1–100 t ha^?1 for shrub sites, and 0.5–10 t ha^?1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r ² = 0.72 when using a one‐step approach (i.e. using all points) and 0.78 when using a two‐step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two‐step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two‐step approach over the one‐step approach, however, is not significant for the validation dataset, suggesting that the one‐step approach is as good as the two‐step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report.     

Estuarine shoreline change detection using Japanese ALOS PALSAR HH and JERS‐1 L‐HH SAR data in the Albemarle‐Pamlico Sounds,North Carolina,USA

An edge extraction model has been developed that uses multitemporal satellite synthetic aperture radar data to delineate shorelines in estuaries. Using Japanese ALOS PALSAR HH data acquired in December 2006 and JERS‐1 L‐HH SAR data in December 1994, we mapped shorelines of the outer Pamlico Peninsula, Dare County, North Carolina, USA, spanning 12 years. To assess the validity and accuracy of the delineation, we compared (a) 1994 shorelines versus those derived from the 1993 digital orthophotographs, and (b) 2006 shorelines versus field data collected in February 2007. With promising results, we then analysed and quantified the shoreline changes between 1994 and 2006 on the north, east, and south sides of the Peninsula. Virtually no discernible changes on the north and south sides were found. However, significant landward migration in the middle to southern portion on the east shore was observed. Spatial retreat of shorelines varied greatly, with the maximum rate averaged over a span of 12 years exceeding 11 m year^?1. The results support further monitoring of shorelines in estuaries using active remote sensing and the potential for the methodology developed to identify erosional hotspots.     

Extreme‐learning‐machine‐based land cover classification

The extreme learning machine (ELM), a single hidden layer neural network based supervised classifier is used for remote sensing classifications. In comparison to the backpropagation neural network, which requires the setting of several user‐defined parameters and may produce local minima, the ELM requires setting of one parameter, and produces a unique solution for a set of randomly assigned weights. Two datasets, one multispectral and another hyperspectral, were used for classification. Accuracies of 89.0% and 91.1% are achieved with this classifier using multispectral and hyperspectral data, respectively. Results suggest that the ELM provides a classification accuracy comparable to a backpropagation neural network with both datasets. The computational cost using the ELM classifier (1.25 s with Enhanced Thematic Mapper (ETM+) and 0.675 s with Digital Airborne Imaging Spectrometer (DAIS) data) is very small in comparison to the backpropagation neural network.     

Evaluating the performance of the space‐borne SAR sensor systems for oil spill detection and sea monitoring over the south‐eastern Mediterranean Sea

Synthetic Aperture Radar (SAR) images have been extensively used for the detection of oil spills in the marine environment as they are independent of local weather conditions, cloudiness and sun illumination. The objective of the study was to provide the users with specific knowledge on SAR image availability over a target area and assess the monitoring capability (visibility of an area) with respect to the requirements for oil‐spill detection and marine environment protection over the south‐eastern part of the Mediterranean Sea. For this purpose, a web GIS tool has been implemented, enabling the users to submit their queries and receive answers in the form of reports and statistics, concerning the current image acquisition capability over the area of interest. It also provides the user with graphic representations of the sensors' swath coverages over the same geographic location. The system has been tested over the Hellenic Seas and the resulting figures denoting the temporal resolution in the observation are analysed and discussed. The analysis shows that the operation of the Envisat satellite, in conjunction with ERS‐2 and Radarsat satellites, has significantly improved the monitoring capability. As shown, the increase in the number of observations over a target location can reach theoretically a level of 130%. In conclusion, the study provides the user with an assessment of the remaining technological gaps and unmet user needs in the domain of marine environment protection.     

Retrieval of sea ice thickness distribution in the seasonal ice zone from airborne L‐band SAR

Although satellite data are known to be useful for obtaining ice thickness distribution for perennial sea ice or in stable thin sea ice areas, their use in the seasonal sea ice zone (SIZ) is still unresolved. In this study, we approached the problem of ice thickness retrieval by using L‐band Synthetic Aperture Radar (SAR). In the SIZ, ice thickness growth is closely related to ridging activity and therefore we expected surface roughness to be correlated to ice thickness. L‐band SAR is suitable for detecting such surface roughness and should be a useful tool for obtaining ice thickness distribution. To verify this correlation, we conducted shipborne electromagnetic (EM) inductive sounding and supersonic profiling observations with an icebreaker, coordinated with airborne L‐band SAR observations in the southern Sea of Okhotsk in February 2005. The surface elevation was estimated by representing the ship's motion with a low‐pass filter. Backscattering coefficients correlated well with ice thickness and surface roughness, defined by the standard deviation of the surface elevation. This result sheds light on the possibility of determining ice thickness distribution in the SIZ.     

A practical method for computing SAR satellite revisit times: application to RADARSAT‐1 and ENVISAT

Many geoscience applications of space‐borne Synthetic Aperture Radar (SAR) imagery require knowledge of the revisit time of the satellite at a location. In this paper, a practical method is presented for computing the revisit times of SAR satellites at any location on Earth. The method is based on the use of maps that give the number of imaging tracks, i.e. the number of satellite ground tracks from which a location can be imaged. With these maps and some basic equations, the revisit time of a SAR satellite or an arbitrary constellation of SAR satellites can be computed easily. As such, the presented method offers an alternative to computer programs that may not be available to everyone and often are tailored to the use of specific satellites. As an illustration of the method, maps with the number of imaging tracks have been computed for RADARSAT‐1 and ENVISAT and their ease of use is shown by means of an example.     

A WWW‐assisted fax system for Internet fax‐to‐fax communication

This paper describes a WWWassisted fax system (WAX) that is developed to provide reliable and enhanced Internet faxtofax communication. It integrates the easytouse WWW interface with conventional faxing procedures, resulting in an Internet fax system which not only circumvents the cost of long distance fax charges but also adds enhanced functionality not otherwise possible. The WAX system comprises two gateways, namely, the FaxIn and the FaxOut Gateways. The FaxIn Gateway accepts fax messages over Public Switched Telephone Network (PSTN) and stores them in a transit database. The system interfaces with the user over the WWW to provide access to his stored faxes, with the basic ability to send them out over the Internet to recipients. The FaxOut Gateway receives fax files from the FaxIn Gateway through the Internet and transmits them out to the intended recipients via the local PSTN. WAX users do not require any additional hardware except for a fax machine and a personal computer with Internet connectivity to gain access to WAX via any WWW browser. In addition, WAX provides a host of other enhanced features such as the ability to construct minifaxes from a single incoming fax as well as dynamically attach cover notes to outgoing faxes.     

Detecting deforestation with multitemporal L‐band SAR imagery: a case study in western Brazilian Amazônia

Applications of L‐band SAR data to map deforestation are generally based on the assumption that undisturbed forests consistently exhibit higher radar backscatter than deforested areas. In this Letter we show that depending on the stage of the deforestation process (slashing, burning and terrain clearing), this assumption is not always valid, and deforested areas may display a stronger radar return backscatter than primary forest. The analysis of multitemporal SAR images, supported by several Landsat Thematic Mapper (TM) images and field knowledge, showed that wood materials left following the deforestation practices function as corner reflectors, causing an initial increase in the radar backscatter, which then subsequently decreases over time as the debris on these fields are removed.     

Biophysical estimation in tropical forests using JERS‐1 SAR and VNIR imagery. II. Aboveground woody biomass

Accurate estimates of aboveground biomass in tropical forests are important in carbon sequestration and global change studies. Tropical forest biomass estimation with microwave remote sensing is limited because of the strong scattering and attenuation properties of the green canopy. In this study a microwave/optical synergistic model was developed to quantify these effects to Synthetic Aperture Radar (SAR) signals and to better estimate woody structures, which are closely related to aboveground biomass. With a Leaf Area Index (LAI) retrieved from Japan Earth Resources Satellite (JERS)‐1 Very Near Infrared Radiometer (VNIR) imagery, leaf scattering and attenuation to woody scattering were quantified and removed from the total backscatter in a modified canopy scattering model. Woody scattering showed high sensitivity to biomass >100 tonnes/ha in tropical forests. Tree height and stand density were derived from the JERS‐1 SAR image with a root mean square error (RMSE) of 4 m and 161 trees/ha, respectively. Aboveground biomass was calculated using a general allometric equation. Biomass in secondary dry dipterocarps (Dipterocarpaceae family of tropical lowland deciduous trees) was overestimated. The modelled biomass in mixed deciduous and dry evergreen forests fit better with ground measurements. In mountainous areas with steep slopes, the topographic effects in the SAR image could not be properly corrected and therefore the results are unreliable.     

Design‐code traceability for object‐oriented systems

Traceability is a key issue to ensure consistency among software artifacts of subsequent phases of the development cycle. However, few works have so far addressed the theme of tracing object oriented (OO) design into its implementation and evolving it. This paper presents an approach to checking the compliance of OO design with respect to source code and support its evolution. The process works on design artifacts expressed in the OMT (Object Modeling Technique) notation and accepts C++ source code. It recovers an “as is” design from the code, compares the recovered design with the actual design and helps the user to deal with inconsistencies. The recovery process exploits the edit distance computation and the maximum match algorithm to determine traceability links between design and code. The output is a similarity measure associated to design‐code class pairs, which can be classified as matched and unmatched by means of a maximum likelihood threshold. A graphic display of the design with different green levels associated to different levels of match and red for the unmatched classes is provided as a support to update the design and improve its traceability to the code.     

Issues in modified problem‐based learning: A self‐study in pre‐service science‐teacher education

Abstract:

In this self‐study, the author gained in‐depth understanding of how to plan and implement problem‐based learning (PBL), a student‐centred approach to teaching and learning that is driven by messy, open‐ended problems. This paper focuses primarily on the issues and concerns that arose as she developed and implemented a modified form of traditional PBL (Barrows, 1996) in large, pre‐service science‐teacher education classes. To view the research from many perspectives, a variety of data collection methods and sources were used, including field notes, semi‐structured interviews, student‐generated documents, and student journals. The outcomes of this study describe challenges (problem development, facilitation of groups, and assessment) encountered by the author as she planned for and implemented PBL. Furthermore, changes in the author's classroom practice, the connection between these changes and constructivist learning principles, and implications for science‐teacher education are addressed.     

Cross‐calibration of MSG1‐SEVIRI infrared channels with Terra‐MODIS channels

This paper addresses the cross‐calibration of the infrared channels 4 (3.9 µm), 9 (10.8 µm) and 10 (12.0 µm) of the Spinning Enhanced Visible and Infra‐Red Imager (SEVIRI) onboard the Meteosat Second Generation 1 (MSG1) satellite with the channels of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Terra. The cross‐calibrations, including the Ray‐Matching (RM) method and the Radiative Transfer Modelling (RTM) method, were developed and implemented over a tropical area using SEVIRI and MODIS measurements of July 2005 and July 2006 with absolute view zenith angle differences (|ΔVZA|)<0.5°, absolute view azimuth angle differences (|ΔVAA|)<0.5° and absolute time differences (|ΔTime|)<10 min. The results obtained by the RM and RTM methods revealed calibration discrepancies between the two sensors. The results obtained by the RM method were consistent with previously published results. The results obtained by the RTM method were consistent with the results obtained by the RM method if the temperature differences caused by the spectral differences between the two sensors were taken into account. From the cross‐calibration results obtained by the two methods, the use of the results obtained by the RTM method to recalibrate the SEVIRI data is recommended. The recalibrations remove the overestimation of the Land Surface Temperature (LST) retrieved from the SEVIRI data by a split‐window method.     

Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery

The extraction of texture features from high‐resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. This study presents the results of grey‐level co‐occurrence matrix (GLCM) and wavelet transform (WT) texture analysis for forest and non‐forest vegetation types differentiation in QuickBird imagery. Using semivariogram fitting, the optimal GLCM windows for the land cover classes within the scene were determined. These optimal window sizes were then applied to eight GLCM texture measures (mean, variance, homogeneity, dissimilarity, contrast, entropy, angular second moment, and correlation) for the scene classification. Using wavelet transformation, up to five levels of macro‐texture were computed and tested in the classification process. Comparing the classification results, (1) the spectral‐only bands classification gave an overall accuracy of 58.69%; (2) the statistically derived 21×21 optimal mean texture combined with spectral information gave the best results among the GLCM optimal windows with an accuracy of 73.70%; and (3) the combined optimal WT‐texture levels 4 and 5 gave an accuracy of 63.56%. The combined classification of these three optimal results gave an overall accuracy of 77.93%. The results indicate that even though vegetation texture was generally measured better by the GLCM‐mean texture (micro‐textures) than by WT‐derived texture (macro‐textures), the results show that the micro–macro texture combination would improve the differentiation and classification of the overall vegetation types. Overall, the results suggests that computer‐assisted classification of high‐spatial‐resolution remotely sensed imagery has a good potential to augment the present ground‐based forest inventory methods.