With the wider availability of more and more images acquired from various satellites, the integration of different orientation models for different sensors has become an important task in order to maintain geometrical consistency. This study combines two heterogeneous geometric correction models, namely, direct georeferencing and the rational function model for multisatellite image positioning. Two types of adjustment models, collocation-based block adjustment (CBBA) and collocation-aided block adjustment (CABA), are proposed to reach the goal. The former is based on the concept that the adjustment is first performed on individual images, and then, all images are integrated. Discrepancies are compensated for by least squares collocation (LSC). The latter method entails simultaneous adjustment of the two heterogeneous models followed by LSC. Experimental results indicate that the proposed method can significantly improve the geometric accuracy as well as reduce geometric discrepancies between images. We also demonstrate that CABA is slightly better than CBBA.

This paper presents an overview of the current status and data applications of FORMOSAT-2, Taiwanese's first earth observation satellite mission. Highlights of its contributions to monitoring of global natural disasters and earth environmental changes will be illustrated. The FORMOSAT-2 satellite successfully complements existing high spatial resolution imaging satellites such as SPOT-5, IKONOS, and QuickBird, among others, with its unique capability of daily revisits worldwide. The FORMOSAT-2 follow-up program to ensure data continuity to the user community is briefly introduced.

To acquire the largest possible coverage for environmental monitoring, it is important in most situations that the overlapping areas and the convergent angles of respective satellite images be small. The traditional bundle adjustment method used in aerial photogrammetry may not be the most suitable for direct orientation modeling in situations characterized by weak convergence geometry. We propose and compare three block adjustment methods for the processing of satellite images using the digital elevation model (DEM) as the elevation control. The first of these methods is a revised traditional bundle adjustment approach. The second is based on the direct georeferencing approach. The third is a rational function model with sensor-oriented rational polynomial coefficients. A collocation technique is integrated into all three methods to improve the positioning accuracy. Experimental results indicate that using the DEM as an elevation control can significantly improve the geometric accuracy as well as the geometric discrepancies between images. This is the case for all three methods. Moreover, the geometric performance of the three methods is similar. There is a significant improvement in geometric consistency between overlapping SPOT images with respect to single image adjustment for steep areas.

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

The main purpose of this investigation is to build up a rigorous procedure to perform georeferencing for Formosat-2 satellite. The proposed scheme comprises two major components: (1) orbit modeling and (2) image orthorectification. In the orbit modeling, instead of bundle adjustment, we propose a collocation procedure to determine the precision orbits. The field-of-view (FOV) of Formosat-2 satellite is so small, i.e. 1.5 degree, that produces extremely high correlation between orbital parameters and attitude data. Thus, using on-board ephemeris data, we iteratively adjust the orbital parameters and attitude data. Then, a least squares collocation technique is applied to collocate the orbit. In the image back projection, we use patch-based approach to accelerate the computation without losing accuracy. A standard scene of 24km by 24km and a strip with some 200km long are tested in the validation.

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.

In the paper, the damage detection and land-use analysis for a major typhoon, called AERE, will be addressed. The damage detection is based on regular monitoring of the land-cover changes in the watershed. A mechanism for periodical data acquisition, ortho-images rectification and land-cover change detection is performed for the reservoir management. The mechanism can also assure a fast response for disaster that happened in the watershed. In the mechanism, an intensive data acquisition in the watershed using the SPOT images with a frequency of three images per week has been achieved. One dataset of every two weeks is selected. Images with less than 50% cloud cover are chosen for ortho-rectification and further for land-cover change detection. When the changes of vegetation indices for multi-temporal images exceed a ceiling, the location is subject to in-situ inspection to prevent deteriorations. Illegal human activities and land-cover changes were successfully supervised and controlled. If a disaster happens and causes damage, the integration of satellite images and relevant geospatial data, such as land-use maps and terrain models will then provide a good solution for damage estimation. The spatial relationship between the damage and the land-use type can thus be analyzed.

The most rigorous way to register a remotely sensed image with a topomap or a relevant spatial data layer is performing orthorectification for the image. The focus of this investigation is to establish a fast orthorectification procedure for satellite images using a proposed "patch backprojection" method. The proposed scheme comprises two major components: (1) orbit modeling and (2) image orthorectification. Pushbroom scanner images including SPOT5, QuickBird, and EROS A are considered in the investigation. Experimental results indicated that the accuracy loss is insignificant in all of the cases while the computation time has been reduced significantly.

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.>