Image stitching is an active research area in many photogrammetric and remote sensing applications. The main contribution of our work is to regard stitching as a multi-image matching problem that exploits advantages of invariant local features and homographic transformation to overcome photometric, geometric, and perspective distortions. Our proposed approach, referred to as Haar Invariant Feature with PROSAC, consists of three main steps. The first step extracts a set of independent keypoints by an adaptive local feature extractor based on Haar wavelet transform and scale invariant feature transform (SIFT). Then to increase the precision of matched points, we propose to substitute classical matching algorithms by a fast Haar-k-nearest neighbor algorithm and estimate the homography matrix using the Progressive Sample Consensus algorithm. Finally, we warp images according to the projective transformation and blend them with a Laplacian multi-band to create a perfect panorama. Experiments on various images show the effectiveness and the robustness of our approach compared to common image matching techniques such as SIFT, speeded up robust features, optimized random sample consensus with contrario, and image matching by affine simulation.