Jiann Yeou Rau

Detection of shoreline changes for tideland areas using multi-temporal satellite images

An original scheme to detect shoreline changes using multi-temporal satellite images and tidal measurements is presented here. First, the basic idea behind this investigation is to reconstruct a reference digital terrain model (DTM) for tideland areas from a set of SPOT satellite images sampled over a short period. Each image corresponds to a tidal measurement. Then, the shoreline, as interpreted from a historical satellite image, is compared with one traced from the reference DTM, according to the associated tidal elevations. Experimental results indicate that the area error of the test sand barriers ranges between 7.6% and 12.5%.

Building reconstruction from LIDAR data and aerial imagery

This paper presents a scheme for building detection and reconstruction by merging LIDAR data and aerial imagery. In the building detection part, a region-based segmentation and object-based classification are integrated. In the building reconstruction, we analyze the coplanarity of the LIDAR point clouds to shape roofs. The accurate positions of the building walls are then determined by integrating the edges extracted from aerial imagery and the plane derived from LIDAR point clouds. The three dimensional building edges are thus used to reconstruct the building models. In the reconstruction, a patented method SMS (Split-Merge-Shape) is incorporated. Having the advantages of high reliability and flexibility, the SMS method provides stable solution even when those 3D building lines are broken. LIDAR data acquired by Leica ALS 40 and aerial images were used in the validation. Experimental results indicate that the successful rate for building detecition is higher that 81%. The positioning for buildings may reach sub-meter accuracy.

Robust reconstruction of building models from three-dimensional line segments

tor has to estimate the location of hidden corners from the conThis paper presents a novel method for semi-automatically jugate images. This procedure is tedious and inefficient and constructing building models from photogrammetric 3D line has a limited accuracy, especially for connected buildings in segments of buildings, i.e., their roof edges. The method, which densely built-up areas. we call “Split-Merge-Shape” (SMS), can treat both complete Weidner (1997), Haala and Brenner (1998), and Brenner line segments as well as incomplete line segments due to image (2000) proposed the use of digital surface models (DSMS )t o reocclusions. The proposed method is comprised of five major construct 3D building models. The DSM data can be generated parts: (1) the creation of the Region of Interest (ROI) and pre- automatically using stereo-pairs, or can be obtained from airprocessing, (2) splitting the model by using the 3D line seg- borne laser scanning (Lohr, 1996). The problem is that precise ments to construct a combination of roof primitives, (3) merging buildingboundaries cannotbewell defineddue tothesegmenconnected roof primitives to complete the boundary of each tation of DSMS. Therefore, other complementary data, such as building, (4) shaping each building rooftop by connected ground plans of the building outlines, are necessary to assure coplanar analysis and coplanar fitting, and (5) quality assur- the reconstruction. This limits the practicability of the ance. The experimental results indicate that the proposed approach. method can soundly rebuild the topology from the 3D line In order to increase efficiency, Fischer et al. (1998) and segments and reconstruct building models with up to a 98

Dynamics Monitoring and Disaster Assessment for Watershed Management Using Time-Series Satellite Images

This paper presents a mechanism that utilizes intensive multitemporal and multisensor satellite images to monitor land cover dynamics. The proposed approach could be applied for regular dynamics monitoring, disaster monitoring and assessment, and vegetation recovery after natural disasters. The disaster monitoring and assessment are the most important issues imbedded in the program. This paper gives an example using the proposed mechanism to cover a major watershed in Taiwan. Often natural hazards such as typhoons or earthquakes trigger landslides or debris flows, which can deliver large amounts of sediment into a reservoir, decreasing its capacity for water storage. Disaster assessment prior to decision-making and support efforts is a must. Three major typhoons that happened in 2004 and 2005 will be discussed here. The proposed mechanism is demonstrated to be feasible, practical, and effective, since with it we are able to generate disaster assessment in a shorter time than with on-site or aerial-photo surveying alone provided that intensive satellite images are available

Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment

In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade, road, tree, and grass. A multicamera airborne imaging system that can simultaneously acquire VAI and OAI is suggested. The acquired small-format images contain only three RGB spectral bands and are used to generate photogrammetric point clouds through a multiview-stereo dense matching technique. To assign each 3-D point cloud to a corresponding urban object class, we first analyzed the original OAI through object-based image analyses. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry- and topology-related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade. Finally, the photogrammetric point clouds were classified into the aforementioned five classes. The classification accuracy was assessed on the image space, and four experimental results showed that the overall accuracy is between 82.47% and 91.8%. In addition, visual and consistency analyses were performed to demonstrate the proposed classification schemes feasibility, transferability, and reliability, particularly for distinguishing elevated objects from OAI, which has a severe occlusion effect, image-scale variation, and ambiguous spectral characteristics.

Semiautomatic Object-Oriented Landslide Recognition Scheme From Multisensor Optical Imagery and DEM

Rainfall-induced landslides are a major threat in Taiwan, particularly during the typhoon season. A precise survey of landslides after a super event is a critical task for disaster, watershed, and forestry land management. In this paper, we utilize high spatial resolution multispectral optical imagery and a digital elevation model (DEM) with an object-oriented analysis technique to develop a scheme for the recognition of landslides using multilevel segmentation and a hierarchical semantic network. Four case studies are presented to evaluate the feasibility of the proposed scheme. Three kinds of remote sensing imagery, namely pan-sharpened FORMOSAT-2 satellite images, aerial digital images from Z/I digital mapping camera, and images acquired by a digital single lens reflex camera mounted on a fixed-wing unmanned aerial vehicle are used. An accuracy assessment is accomplished by evaluating three test sites containing hundreds of landslides associated with the Typhoon Morakot. The input data include ortho-rectified image and DEM. Four spectral and one topographic object features are derived for semiautomatic landslide recognition. The threshold values are determined semiautomatically by statistical estimation from a few training samples. The experimental results show that the proposed approach can counteract the commission/omission errors and achieve missing/branching factors at less than 0.12 with a quality percentage of 81.7%. The results demonstrate the feasibility and accuracy of the proposed landslide recognition scheme even when different optical sensors are utilized.

Reconstruction of building models with curvilinear boundaries from laser scanner and aerial imagery

This paper presents a scheme to detect building regions, followed by a reconstruction procedure. Airborne LIDAR data and aerial imagery are integrated in the proposed scheme. In light of the different buildings, we target the ones with straight and curvilinear boundaries. In the detection stage, a region-based segmentation and object-based classification are integrated. In the building reconstruction, we perform an edge detection to obtain the initial building lines from the rasterized LIDAR data. The accurate arcs and straight lines are then obtained in the image space. By employing the roof analysis, we determine the three dimensional building structure lines. Finally, the Split-Merge-Shape method is applied to generate the building models. Experimental results indicate that the success rate of the building detection reaches 91%. Among the successfully detected buildings, 90% of the buildings are fully or partially reconstructed. The planimetric accuracy of the building boundaries is better than 0.8m, while the shaping error of reconstructed roofs in height is 0.14 m.

A unified solution for digital terrain model and orthoimage generation from SPOT stereopairs

An original scheme to automatically generate digital terrain models (DTMs) and orthoimages from SPOT stereopairs in a unified way is presented. In addition to modeling the time-dependent orientation parameters, an algorithm is developed to generate the epipolar stereomate incorporating an initial DTM. The refining of the DTM is then accomplished by performing least-squares template matching along each conjugate epipolar line pair, space intersection, and grid value interpolation. Recursively, the epipolar stereomate is regenerated according to the refined DTM. The procedure is repeated until the disparities between the two stereomate images are small enough. The DTM generated illustrates the apparent surface. It is more correct to use this apparent DTM than the one depicting the ground surface for the geometric correction for remotely sensed data. Both images of the final epipolar stereomate are actually orthoimages because they are geometrically identical and georeferenced. Experimental results indicate that the orthoimages reach an accuracy up to 2/3 pixel. >

Direct Sensor Orientation of a Land-Based Mobile Mapping System

A land-based mobile mapping system (MMS) is flexible and useful for the acquisition of road environment geospatial information. It integrates a set of imaging sensors and a position and orientation system (POS). The positioning quality of such systems is highly dependent on the accuracy of the utilized POS. This limitation is the major drawback due to the elevated cost associated with high-end GPS/INS units, particularly the inertial system. The potential accuracy of the direct sensor orientation depends on the architecture and quality of the GPS/INS integration process as well as the validity of the system calibration (i.e., calibration of the individual sensors as well as the system mounting parameters). In this paper, a novel single-step procedure using integrated sensor orientation with relative orientation constraint for the estimation of the mounting parameters is introduced. A comparative analysis between the proposed single-step and the traditional two-step procedure is carried out. Moreover, the estimated mounting parameters using the different methods are used in a direct geo-referencing procedure to evaluate their performance and the feasibility of the implemented system. Experimental results show that the proposed system using single-step system calibration method can achieve high 3D positioning accuracy.

LOD generation for 3d polyhedral building model

This paper proposes an algorithm for the automatic generation of Levels-of-Detail (LODs) for 3D polyhedral building models. In this study a group of connected polyhedrons is considered as “one building”, after which the generalization is applied to each building consecutively. The most detailed building models used is the polyhedral building model which allows for an elaborate roof structure, vertical walls and a polygonal ground plan. In the work the term “Pseudo-Continuous LODs” is described. The maximum distinguishable “feature resolution” can be estimated from the viewer distance to a building and is used to simplify the building structure by the polyhedron merging and wall collapsing with regularization processes. Experimental results demonstrate that the number of triangles can be reduced as a function of the feature resolution logarithm. Some case studies will be presented to illustrate the capability and feasibility of the proposed method including both regular and irregular shape of buildings.