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The <italic>spatial details</italic> injection model has been considered as a general framework in the literature of <italic>pansharpening</italic>, and recently there have been significant advances in this framework based on <italic>sparse representation</italic> (SR) of spatial details. However, the SR-based methods have greater computational burden in estimating the sparse vectors and limited ability in detail edge preservation. In this article, we introduce the <italic>neighbor embedding</italic> (NE) instead of SR-based model and the edge-preserving filter into the spatial detail injection framework to address the aforementioned two drawbacks. By utilizing the best quality of NE, we propose the <italic>detail injection via NE</italic> (DINE) algorithm for pansharpening, and DINE+, an improved variant of DINE by using the edge-preserving filter to enhance the spatial details. Experiments carried on three datasets captured by different satellite sensors and compared with current state-of-the-art methods validate the effectiveness of the proposed methods.