This paper presents our research on developing a dense image matching algorithm to generate a high-quality disparity map from airborne oblique stereo images. We conducted off-site measurements and analysis not on models but on the actual aerial photography of a site. The Oblique Photogrammetry System（ OPS） was influenced by and significantly affected photogrammetry.Oblique images released by OPS are becoming an indispensable tool for general use

such as tax assessment and building deviation

urban and infrastructural planning

management of military and security operations

and critical infrastructural protection. However

processing oblique images is quite challenging in terms of automation and accuracy. For example

oblique image stereo matching is subjected to various obstacles

such as obvious illumination differences

serious occlusions

discontinuous object boundaries

and low or repetitive textures. To address such problems

an image matching approach based on the integration of improved SGM and SIFT algorithm is proposed to generate a dense disparity map from an airborne oblique image pair

which establishes a basis for automatic photogrammetry Digital Surface Model（ DSM） generation or Computer Vision three dimensional visualization.The proposed approach is composed of two stages:（ 1） The first stage is sparse oblique image matching with improved SIFT algorithm

in which the affine invariance of the SIFT descriptor is enhanced by implementing local two-order moment transformation to feature neighbors detected by using Hessian-Gabor algorithm. Thus

robust matching results for various wide-baseline oblique image pairs are obtained and used to estimate epipolar geometry model or as path constraints incorporated into SGM in next stages.（ 2）The second stage is dense oblique image matching with constrained SGM algorithm

in which mutual information from matched features are used as an unchanged"anchor"to block the propagation of mistaken matching cost along some SGM paths and based on TPS transformation. Discrete parallax from matched features is used to generate a continuous disparity map for reliable SGMmutual information computation. In addition

to generate the epipolar image input into SGM

an ideal photogrammetry image leveling algorithm is extended to oblique image pairs with various wide-baseline conditions by globally rotating wide-aseline vector to be horizontal. Then

orientation parameters of oblique image pairs are estimated with a matched feature coordinate by using photogrammetry relative orientation process. Selected five airborne oblique images from OPS with cameras positioned in the configuration of a Maltese cross are tested by using the proposed approaches. Thus

a high-quality dense disparity map is obtained. The following results were obtained:（ 1） The incorporation of Hessian-Gabor feature detector and local two-order moment transformation into the SIFT algorithm greatly enhances its affine invariance. Thus

reliable and rich conjunctive pixels can be obtained in various widebaseline oblique image pairs

and these pixels are intended to be used as strong constraint for further dense matching.（ 2）Improved SGM could block the propagation of mistaken matching and accelerate path searching by means of mutual information and discrete parallax knowledge from known conjunctive pixels. Thus

a high-quality dense match result from oblique images can be obtained.