How to cull shadows and extract needed information accurately is particularly significant. For major remote sensing applications, it may be preferable that shadows are minimized and the detailed information in high-resolution satellite imagery is clear. Firstly this paper reviews some of basic methods of detecting and removing shadows, and outlines their disadvantages. Then taking Nanjing city as study area, we propose a novel method combing spatial-distribution relation with classification to detect building shadows from IKONOS imagery. When detecting and extracting shadows, a majority index based on neighborhood analysis is provided, and a 5-meter buffer analysis is operated after supervised classification. When removing the shadows, a piecewise linear contrast stretch and histogram match are used. The results show that the accuracy of shadows detection and extraction is 92.3%, but texture analysis is 88.1%, and the detail information within shadows regions is enhanced, and there are no bright edges around shadows regions by applying the techniques developed in this paper.