Among the most important types of information contained in radar and optical images are the textural features. In real life images these features are partially masked by noise that is always present in registered data, basically multiplicative in radar images and additive in optical ones. Thus, one of the basic steps in processing remote sensing data is the filtering of the observed image. However, despite the fact that a lot of filters have been already designed, relatively little attention has been paid to texture preservation properties of noise attenuation methods. Thus, there are the following actual tasks: 1) to analyze the texture preservation properties of different filters; 2) to design image processing methods that are able to preserve texture features simultaneously with effective noise suppression. In this paper, the texture feature preserving characteristics of different filters are examined using a set of texture samples, different noise levels and a set of parame-ters including spatial correlation and higher order statistics. The traditional locally adaptive two-state hard switching filters are modified to the three-state ones where texture is considered as a particular class. For “detection” of texture regions, special, rather simple, classifiers that are based on joint analysis and processing of the two local activity indicators are proposed. The recommendations concerning the parameter setting of the classifiers are given. All this provides an appropriate trade-off of the designed filter properties. It improves the PSNR for entire image in comparison to the component filters used within the three-state local adaptation framework. Local PSNRs for the considered types of image fragments are practically the same or even better than for the filter type recommended for the processing of the corresponding classes. Real life image examples are presented to demonstrate the efficiency of the proposed filter.