Two improved local-region filters, adaptive weighted filter (AWF) and collaborative representation filter (CoRF), are proposed for feature extraction and classification in hyperspectral imagery. The local-region filters generate spatial-spectral features of a hyperspectral pixel by incorporating its surrounding pixels. The work of this paper is an extension of our previously introduced local average filter (LAF). Unlike LAF, which gives the surrounding pixels the same weight, AWF and CoRF explore the internal similarity in the local region with an adaptive weight. More specifically, AWF is set up considering the spatial distance to the central pixel, and CoRF is constructed with spectral similarities adopting the idea of collaborative representation. The two improved local-region filters adaptively extract spectral-spatial features from neighboring pixels and are proven to be effective in many aspects, such as edge information preservation and classification performance, with experiments on two real hyperspectral datasets.