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The development of high-performance classifiers represents an important step in improving the timeliness of remote sensing classification in the era of high spatial resolution. The geographically weighted k-nearest neighbors (gwk-NN) classifier, which incorporates spatial information into the traditional k-NN classifier, has demonstrated better performance in mitigating salt-and-pepper noise and misclassification. However, the integration of spatial dependence into spectral information is computationally intensive. To improve the computing performance of the gwk-NN classifier, this study first considered two commonly used parallel strategies—data parallelism and task parallelism—in the model training and image classification stages. Then, our implementation of the corresponding parallel algorithms was carried out by calling message passing interface (MPI) and the geospatial data abstraction library (GDAL) in the …