نشریه علوم و فنون نقشه برداری
سال دوم شماره 4 (پیاپی 8، اردیبهشت 1392)

The automatic change detection in urban environment using multitemporal high resolution satellite images is one of the fundamental processes in photogrammetry and remote sensing. Conventional approaches to automated building change detection relied to multitemporal images comparison based on multispectral classification methods. These approaches require accurate alignment between the two images to detect changes. Accurate registration of high resolution satellite images is a difficult process due to local distortion in these images due to relife displatement. This paper introduces a multi temporal image processing approach towards an efficient and automated detection of urban changes. The proposed method is based on local features obtained from sequential images including corners, blobs and regions. In the first step, local features are extracted in each of the images using three well-known feature extractor operator incuding phase congruency, UR-SIFT method and MSER algorithm. Then, feature matching process in a gridding structure is performed between two feature sets using SIFT descriptor. This process followd by a new mismatch elimination method based on distance ratio. In the next step, a similarity image based on a new measure is estimated between multitemporal images. In final, using an automatic threshold changed regions are determined. The method has been evaluated by using QuickBird and World Wiew image for the area of the city of Sun francisco, USA. Experimental results prove the capabilities of the proposed change detection algorithm with appropriate accuracy in multitemporal high resolution satellite images.

Our purpose in this contribution is to compare different outlier detection methods as far as time series are concerned. In fact, three methods, namely wavelet analysis, Baarda data snooping and thresholding are investigated. In order to make reasonable comparisons among the performance of these three methods in detecting the outliers, we used 4-month synthesized time series based on real tidal data. When the functional model of observations is known, Baarda data snooping, in comparison with other two methods yields the best results since its outlier rate of success and outlier rate of failure are almost 100% and 0.64%, respectively, regardless of the type of outliers. Furthermore, if the functional model of observations is unknown, wavelet analysis perform better than thresholding.

Landslides, one of the major geo hazards, always cause a major problem by killing hundreds of people every year besides damaging the properties and blocking the communication links. In order to mitigate hazards of mass failure, the first step is the production of a landslide susceptibility map. Landslide inventory maps can play important role to prepare landslide susceptibility maps. field observations for preparing these maps are very costly and time consuming also Aerial photos can’t be acquired regularly and don’t have global cover, old satellite images have had low spatial resolution and needed to have control points in order to correct geometric errors that was the reason landslide replacements have approximately been recognized by spectral analysis on that time. The recent improvements in the high resolution satellite image technology, has provided a possibility of geometric corrections with rational coefficient and georeferencing with just one ground control point using GPS/INS/Star-tracker installed in the satellite. By high resolution satellite images, we’ll be able to detect replacements of ground which is the important parameter to provide landslide prediction models. In this paper, four epochs of the high resolution satellite images IRSP5 in Damdol village, Ardebil Province, Iran, are used to detect landslide ground replacements. Our results that are compared with GPS data which are acquired by precise Istasanj consultant engineering show the calculated discrepancies are less than half meter.

The mean sea level (MSL) trends measured by tide gauges are contaminated by transient effects (caused by atmospheric pressure variations, interannual and decadal changes). In this paper, the assessment and reduction of these effects are performed by the utilization of two different cumulative sum (associated with bootstrap resampling scheme to get access reliable confidence intervals for the estimated results) and using time-frequency analysis with the continuous wavelet transform (CWT). The numerical results are illustrated using the tide gauge data, Bridgeport in the USA (over a time period of 47 years) and another nearby station series, New York City (over a time period of 106 years) to demonstrate the ability of the methods in detection of transient effects in the MSL. Due to the ability of the CWT to construct a time-frequency representation of a signal in different detail or resolution, this approach could be used as a powerful tool for detection of the transient effects in tide gauge time series. The numerical results show that the significant reduction of the residuals when using of CWT with respect to the using of cumulative sum method.

Object detection and tracking in image sequences is a key topic in photogrammetry and computer vision and can be viewed as a lower level vision tasks to achieve higher level event understanding. Images from different kind of sensors generally have different pixel characteristics due to the phenomenological differences between the image formation process of the sensors. In recent years, some approaches have already been proposed to detect and recognize moving objects in thermal Infra- Red (IR) image sequences, especially in the field of monitoring, security and surveillance. Nevertheless, tracking of multiple moving objects in forward looking thermal IR image sequences can be complex due to loss of information caused by projection of the 3D world on a 2D image, noise in images, complex object motion, non-rigid or articulated nature of objects, partial and full object occlusions, complex object shapes, scene illumination changes, and simultaneous changing of the situationof objects and sensor. This paper presents a novel method that overcomes most of the shortcomings of the existing detection and tracking algorithms in forward looking thermal IR image sequences. In this context, the Speeded-Up Robust Features (SURF) detector, Nearest Neighbor (NN), and RANdom SAmple Consensus (RANSAC) have been used to eliminate motion induced by the motion of the platform. Next, the Accumulative Frame Difference (AFD) has been used to detect moving objects from the ego-motion compensated input sequences. Also, an outlier removal algorithm based on Mean Gray Area (MGA), compactness, and eccentricity has been applied to detect and remove non-moving objects induced by errors in alignment, parallax, and etc. Finally, a tracking algorithm based on a constant velocity motion model, and various cues for object correspondence has been applied to perform tracking moving objects using their motion histories. The potential of the proposed method was evaluated through comprehensive experimental tests conducted on a wide variety of datasets. We compare the performance of our detection and tracking setup against different evaluation metrics, namely Hit Rate (HR), False Alarm Rate (FAR), Multi Object Detection Precision (MODP), and Multi Object Tracking Precision (MOTP) for a subset of ten sequences from our datasets. Inspecting the results, the proposed method has the potential to track moving of different pedestrians and cars objects with different motion characteristics effectively and efficiently.

Concepts of multiple reference stations—Virtual Reference Station (VRS) for instance—have been developed during the last two decades to overcome the traditional RTK deficiencies. The basic idea behind the VRS method is the application of spatiotemporal dependence of errors to reduce the effects of biases on virtually generated observations. This amelioration improves the final coordinates and reduces the number of permanent stations settlement over a regional network. The virtual station must be as close as possible to the rover station. For this purpose, the rovers have to transmit their approximate coordinates to a data process center (the master reference station), which can be obtained using the navigation solution. The approximate coordinates of the rover are then selected as the position of the virtual station. Therefore the virtual observations can be generated at the VRS position and corrections due to the dispersive and nondispersive biases are implemented to the observations. This generation of virtual observations improves the final coordinates and reduces the number of permanent stations settlement over a regional network. In this article the VRS generation algorithm is developed and applied to six GPS stations of NGS (National Geodetic Survey) permanent network. Various error interpolation methods are tested for the VRS algorithm efficiency. The results prove that the VRS algorithm works correctly, which can be used for regional and national networks. The results were shown not much to be dependent on the choice of the interpolation method. However, the error mitigation algorithm plays the most important role.

Nowadays, digital terrain models (DTM) are an important source of spatial data for various applications in many scientific and commercial disciplines. Therefore, special attention is given to their main characteristic ‐ accuracy. These models are infected with various types of errors in the process of sampling, measurement and reconstruction. These errors are divided in to three groups: random, systematic and gross errors. As it is well known, the source data for DEM creation contributes a large amount of errors, including gross errors (blunders), to the final product, which are unacceptable for a practical project. Therefore, the detection and deletion of gross error from DTM data has been becoming a great concern in geospatial data analysis. Most of existing approaches are based on statistical tests and present considerable problems for isolating observations and avoiding their influence. This paper presents an algorithm based on robust estimation with IRLS. Also, the application of robust methods to digital terrain modeling is analyzed versus the classical least-squares approach. Entire dataset is divided into some separate patches. In each patch a bilinear surface is fit to fully surrounded points and the residual for each point is estimated. By the use of robust estimation, it is tried to minimize the sum of squared residuals in order to detect points with gross error. The results showed that the proposed method provides a maximum-resistance solution and therefore the capability of identifying blunders.