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Data conflation refers to the methods and processing of information fusion whereby multi-source data are integrated to derive required information that is thought to be of increased accuracy, finer resolution, better homogenised semantics, fuller coverage, enhanced representational and computational efficiency, or improved utility for certain purposes than any single data source alone. As spatial data can be conceived of as realisations of random fields, multivariate geostatistics provides a coherent framework for data conflation. As important metadata of spatial data, scale, which is considered synonymous with data support in this paper, and semantics, which concern the meanings given to ‘elevation’ in elevation data, for example, are complicating factors for data conflation, and need to be handled properly. This paper’s novelty lies in its handling of semantic differences via corrections to biases in local means, and its trend-residual decomposition-based structural modelling of terrain elevation data, ...

Data refinement refers to the processes by which a dataset’s resolution, in particular, the spatial one, is refined, and is thus synonymous to spatial downscaling. Spatial resolution indicates measurement scale and can be seen as an index for regular data support. As a type of change of scale, data refinement is useful for many scenarios where spatial scales of existing data, desired analyses, or specific applications need to be made commensurate and refined. As spatial data are related to certain data support, they can be conceived of as support-specific realizations of random fields, suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finer-resolution ones. In this paper, geostatistical methods for downscaling are described, and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China, with the latter’s majority grid cells used as surrogate reference data. It was found ...

This paper develops a novel hybrid model that integrates three spatial contexts into probabilistic classifiers for remote sensing classification. First, spatial pattern is introduced using multiple-point geostatistics (MPGs) to characterize the general distribution and arrangement of land covers. Second, spatial correlation is incorporated using spatial covariance to quantify the dependence between pixels. Third, an edge-preserving filter based on the Sobel mask is introduced to avoid the over-smoothing problem. These three types of contexts are combined with the spectral information from the original image within a higher-order Markov random field (MRF) framework for classification. The developed model is capable of classifying complex and diverse land cover types by allowing effective anisotropic filtering of the image while retaining details near edges. Experiments with three remote sensing images from different sources based on three probabilistic classifiers obtained results that significantly improved classification accuracies when compared with other popular contextual classifiers and most state-of-the-art methods.

A post-processing method for increasing the accuracy of a remote sensing classification was developed and tested based on the theory of multiple-point geostatistics. Training images are used to characterise the joint variability and joint continuity of a target spatial pattern, overcoming the limitations of two-point statistical models. Conditional multiple-point simulation (MPS) was applied to a land cover classification derived from a remotely sensed image. Training data were provided in the form of “hard” (land cover labels), and “soft” constraints (class probability surfaces estimated using soft classification). The MPS post-processing method was compared to two alternatives: traditional spatial filtering (also a post-processing method) and the contextual Markov random field (MRF) classifier. The MPS approach increased the accuracy of classification relative to these alternatives, primarily as a result of increasing the accuracy of classification for curvilinear classes. Key advantages of the MPS approach are that, unlike spatial filtering and the MRF classifier, (i) it incorporates a rich model of spatial correlation in the process of smoothing the spectral classification and (ii) it has the advantage of capturing and utilising class-specific spatial training patterns, for example, classes with curvilinear distributions.

This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.

Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

Remotely sensed mineralized alteration information is of great significance to geological prospecting and extracting such alteration information in high vegetated areas is difficult. Based on ASTER data, the following processes are performed. First, the ASTER data are geometrically corrected and atmospherically corrected, and water and shadow are masked. Then, principal component analysis method is applied to extract ferric contamination information, which is enhanced and classified by thresholding. Finally, mineralization anomaly regions are delineated with reference to known ore deposits and related alterations. In the fieldwork, the extracted alteration information was verified, proving that the ASTER data are of capacity to acquire minerals' spectral characteristics in the short wave infrared range, which effectively facilitate geological prospecting in densely vegetated regions.

How to extract the landslides triggered by earthquakes rapidly and accurately is of great significance in current earthquake remote sensing application. Based on the earthquake emergency response, an automatic seismic landslide detection method is proposed in this paper. In the method, a simple and effective automatic thresholding algorithm is designed by using a single-temporal remote sensing image, which is different from traditional change detection methods. The gray scale, shape and texture features of landslides are taken into account in landslide detection, along with several rules and experts' knowledge. In the experiments using high-resolution aerial images, major influencing objects, such as lakes, roads, buildings and cultivated land are effectively removed and more than 80% seismic landslides are extracted successfully. Different from traditional change detection methods based on images before and after the earthquake, the simple and efficient method proposed in the paper is only based on seismic-post remote sensing imagery and is more suitable for earthquake disaster emergency responses.

This study presents a comprehensive deformation retrieval method based on the multi-baseline synthetic aperture radar interferometry InSAR technique for ground movement monitoring. Both the displacement pattern in at the large scale and detailed deformation for local areas are investigated by exploiting two sets of data generated at low and full spatial resolution. In particular, we focus on the description and estimation of non-linear deformation in full-resolution signals. A polynomial parameter model is built and the total least squares-singular value decomposition algorithm is proposed to control the overall errors of the system. The ascending Advanced Land Observing Satellite ALOS Phased Array type L-band SAR PALSAR data set on Tianjin China is used for the algorithm experiment and detailed displacement information between July 2008 and August 2010 is obtained. The experimental results are validated by levelling data and field surveys, and the good agreement proves the effectiveness of the algorithm.

It is important to describe misclassification errors in land cover maps and to quantify their propagation through geo-processing to resultant information products, such as land cover change maps. Geostatistical simulation is widely used in error modeling, as it can generate equal-probable realizations of the fields being considered, which can be summarized to facilitate error propagation analysis. To fix noninvariance in indicator simulation, discriminant space-based methods were proposed to enhance consistency in area-class mapping and replicability in uncertainty modeling, as the former is achieved by imposing means while the latter is ensured by projecting spatio-temporal correlated residuals in discriminant space to geographic space through a mapping process. This paper explores discriminant models for error propagation in land cover change detection, followed by experiments based on bi-temporal remote sensing images. It was found that misclassification error propagation is effectively characterized w...

The DInSAR technique with a multibaseline is becoming popular nowadays to investigate slow urban deformation. In this paper, we focus on deformation retrieval in large areas, including urban and suburban areas. Based on the multibaseline DInSAR algorithm proposed by Mora, three extensions are derived. First, least-squares adjustment and error-controlling methods are used to obtain stable deformation velocity and height error estimations. The least-squares QR factorizaiton algorithm is emphasized to solve large, linear, and sparse functions. Second, a new complex network is presented to limit noise effects on the Delaunay triangular network. Third, by combining complex and Delaunay networks, large-area deformation is investigated, from centre urban areas to suburban areas. The enhanced algorithm is performed to investigate the subsidence of Cangzhou, Hebei province (northern China) during 1993-1997 by using 9 ERS SLC data. The experimental results show serious subsidence in the region and are validated by levelling data and groundwater wells data. Compared with levelling data, the estimation errors of linear deformation velocity in urban areas are in the range of (-2, 2) mm year-1, and in suburban areas, the errors are in the range of (-26, 15) mm year-1, which is sufficiently feasible to determine the status of subsidence relative to the maximum deformation velocity of about -100 mm year-1. The subsidence centres in urban areas are consistent with the spatial distribution of groundwater wells, which provides evidence that groundwater overexploitation is the main cause of subsidence in Cangzhou. The closure of wells will be a good way to control subsidence in the future.

Various multispectral (MS) and panchromatic (PAN) fusion (or pan-sharpening) algorithms were developed to produce an enhanced MS image of high spatial resolution. Regarding the novelty in both the PAN and MS bands of the WV-2 imagery, the objective of this study is to assess the performance of nine state-of-the-art pan-sharpening methods for the WV-2 imagery, using both image quality indices and information indices that used for urban information extraction. The comparison of the four quality indices (RASE, ERGAS, SAM, and Q4) demonstrated that the HR method performed the best for the WV-2 MS and PAN images. However, the comparison of the four information indices showed that a higher quality at data level does not signify better information preservation for object recognition.

In this article, a newsegmentation method based on traditional region growing (RG) is proposed for high resolutionremote sensing imagery. This method takes regional minima from horizontal and vertical gradient maps of the image as seeds for the following region growing processing. The new method consists of several steps as follows: (1)deriving a morphological gradient map from the input multispectral image, (2) morphologically filtering the gradient image to remove local minima with small depthand extracting regional minima of flat areas in the resulting filtered image as seeds, (3) segmenting the multispectral image using the RG approach with reference to the seeds, and (4) merge the resulting initial segments to yield asegmentation map. In a test with a WorldView-2 multispectral image, the proposed method offered segmentation maps with nearly the same accuracy as several current methods.

Over the past quarter century China has moved from a state-controlled economy to one that relies increasingly on market-based financing. The development of accounting principles and regulation has escalated since the trading of shares began in 1990 ( Lin & Feng, 2001 ). Some descriptive studies portray the extent to which published financial statements comply with readily observable disclosure requirements ( Xiao, 1999 ). However, there is little evidence about how disclosure judgments are made. Some observers indicate that experimental evidence may be most appropriate in determining how accounting standards are applied, particularly in different cultures and with different incentives ( Nelson, 2003 , Pownall & Schipper, 1999 ). The current study gathers judgments from over 200 experienced accountants facing mixed incentives for reporting required related party disclosures. The incentives are derived from conditions distilled from China’s environment to develop two realistic cases. To set expectations about disclosure judgment, theoretical arguments are provided about China’s setting relative to settings in countries that have considerable history with market-based financing. Internal accountants (chief accounting officers – CAOs) and external auditors (CPAs) provide their judgments about the same cases to study how the incentives within their roles affect their judgments. One case portrays a CAO and one a CPA to allow for detection of how the participating practitioners perceive the different roles affecting expected disclosure behavior. Irrespective of respondent status, results show that conflict-laden incentives are likely to result in less than full disclosure of required related party information. Practicing auditors report that the CPA auditor portrayed in the cases will more likely insist on disclosure than the CAO portrayed in the cases. This finding is driven by the participating CPAs reporting a higher probability that the CPA portrayed in the cases would insist on disclosure to a greater extent than the CAO portrayed in the cases. This finding suggests that auditors are likely to improve the quality of financial reporting in China.

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

The aim of data conflation is to synergise geospatial information from different sources into a common framework, which can be realised using multivariate geostatistics. Recently, multiple-point geostatistics (MPG) has been proposed for data conflation. Instead of the variogram, MPG borrows structures from the training image, so the spatial correlation is characterised by multiple-point statistics. In pattern-based MPG, two sets of data can be integrated by utilising the secondary data as a locally varying mean (LVM). The training image provides a spatial correlation model and is incorporated to facilitate reproduction of similar local patterns in the predicted image. However, the current patternbased MPG gathers similar patterns based on a prototype class, which extracts spatial structures in an arbitrary way. In this paper, we proposed an improved pattern-based MPG for conflation of digital elevation models (DEMs). In this approach, a new strategy for forming prototype class is applied, which is based on the residual surface, vector ruggedness measure (VRM) and ridge valley class (RVC) of terrain data. The method was tested on the SRTM and GMTED2010 data. SRTM data at the spatial resolution of 3 arc-second was simulated by conflating sparse elevation point data and GMTED2010 data at a coarser spatial resolution of 7.5 arc-second. The proposed MPG method was compared with the traditional pattern-based MPG simulation. Several kriging predictors were applied to provide LVMs for MPG simulation. The result shows that the new method can achieve more precise prediction and retain more spatial details than the benchmarks.

Land surface deformation evidently exists in a newly-built high-speed railway in the southeast of China. In this study, we utilize the Small BAseline Subsets (SBAS)-Differential Synthetic Aperture Radar Interferometry (DInSAR) technique to detect land surface deformation along the railway. In this work, 40 Cosmo-SkyMed satellite images were selected to analyze the spatial distribution and velocity of the deformation in study area. 88 pairs of image with high coherence were firstly chosen with an appropriate threshold. These images were used to deduce the deformation velocity map and the variation in time series. This result can provide information for orbit correctness and ground control point (GCP) selection in the following steps. Then, more pairs of image were selected to tighten the constraint in time dimension, and to improve the final result by decreasing the phase unwrapping error. 171 combinations of SAR pairs were ultimately selected. Reliable GCPs were re-selected according to the previously derived deformation velocity map. Orbital residuals error was rectified using these GCPs, and nonlinear deformation components were estimated. Therefore, a more accurate surface deformation velocity map was produced. Precise geodetic leveling work was implemented in the meantime. We compared the leveling result with the geocoding SBAS product using the nearest neighbour method. The mean error and standard deviation of the error were respectively 0.82 mm and 4.17 mm. This result demonstrates the effectiveness of DInSAR technique for monitoring land surface deformation, which can serve as a reliable decision for supporting highspeed railway project design, construction, operation and maintenance.

The case study presents an information index model for landslide susceptibility mapping in Tonggu County, Jiangxi Province, China. More than 100 landslides were identified from the 2.5-m fused SPOT imagery, and about 60 percent of them were verified in field investigation. It is proved that the landslides are more likely to occur in granite areas, which are strongly weathered, and the areas close to faults. The high elevation accuracy of 75.6% was reached using a receiver operating characteristic (ROC). The result indicates that the south and parts of the northeast of Tonggu County, including Paipu-Yongning and Daduan-Guqiao Towns, are highly susceptible to damages from landslides.

This paper explores data-driven methods for quantifying and incorporating spatio-temporal contextual information in the mapping of land cover change. In remote sensing, area classes of land cover are typically mapped via statistical manipulation of feature-space measurement, e.g., reflectance data, and other ancillary data. Contextual information has been known to have the potential of increasing the accuracy of land cover classification and change detection, on the ground that land cover often exhibits spatial and temporal correlations and, as such, should be properly accommodated. In Bayesian methods, a priori probabilities of class occurrences can be considered as contextual information, which are combined with class-conditional probability densities to arrive at discriminant decisions with minimized misclassification. These prior probabilities may be made to vary locally to honor variability in the strengths of spatial dependence in class occurrences. For deriving local prior joint probabilities in land cover co-occurrences over time, a modified Expectation and Maximization (EM) algorithm was developed, in which a local window size can be adjusted in the light of spatial dependences inferred from class probability densities computed from spectral data. Empirical studies were performed using bi-temporal Landsat TM image subsets in Wuhan, which confirmed the comparative benefits of incorporating localized prior probabilities in land cover change detection.