Human land use is the main driver of terrestrial ecosystems change, and remote sensing is an important tool to monitor these changes. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images have been the most important data source to map land cover change, but image artifacts often hinder or even prohibit digital change detection. This paper addresses a group of image distortions that display erroneous values in a single band while leaving the other bands of a spectrum undisturbed. Such artifacts may be due to different phenomena, for instance transmission and ground-processing problems or single event upsets. Automated artifact detection for those phenomena is often difficult, because erroneous band values often lie well within the range of naturally occurring radiance values. We developed IDL-based software that uses edge operators to detect and label affected pixels. Using a least-squares spectral-matching algorithm, the distorted spectrum is compared with undisturbed spectra in the local neighborhood and the undisturbed spectrum of best fit is determined. The erroneous band value is then replaced with the corresponding undisturbed value. This method was tested on seven Landsat TM images and on artificial data. Our results show that the distorted areas are precisely detected and that the correction procedure leads to meaningful spectra. This approach may be useful to minimize the effect of single-band distortions and allows for subsequent image analysis without the need to mask out distorted areas. The software tool includes a user interface and is available online.