Multiclass geospatial objects detection within complex environments is a challenging problem in remote-sensing areas. In this paper we propose a novel, generalized kernel-based learning framework for the purpose of enhanced object detection. There are two novel areas. (1) Multisource information, including shape, feature points, and appearance, was extracted to give a comprehensive representation of the objects. We improved a shape descriptor and introduced a two-level spatial pyramid to represent appearance, both global and local. Therefore, basis kernels were formed, one for each feature. (2) In order to illustrate the effect of each kind of feature on each pyramid level, a generalized and weighted combination method was first used to combine all of the levels and then the features. The weights and the classifier model are based on the support vector machine framework for obtaining balance between all basis kernels. This classifier was transformed into a powerful detector by using a sliding window. The reported results are for the detection on high-resolution remote-sensing images. This study demonstrates that the proposed generalized and weighted combination of kernels can yield better performance compared with traditional single-kernel classifier and other combination methods.