DBKNet: A Dual-Encoder KAN Segmentation Network for Joint Extraction of Photovoltaic Power Stations and Impervious Surfaces in Arid Regions

Photovoltaic power stations and impervious surfaces are difficult to distinguish from spectrally similar arid-region backgrounds, and their large differences in scale and spatial form further complicate joint extraction. This study proposes DBKNet, a dual-encoder semantic segmentation network for six-band Landsat imagery. ResNetV1c and BiFormer Tiny are used to capture local details and long-range context, respectively. ConvSwinMerge integrates the two feature streams, while a KAN-based decoder and D2T TransformerBlock improve multi-scale representation and contextual recovery. A three-class dataset containing background, impervious surfaces, and photovoltaic power stations was constructed from the 2025 Landsat composite of Ordos. DBKNet achieved an mIoU of 83.40%, an mDice of 90.26%, an overall pixel accuracy of 98.96%, and a Target-mIoU of 75.64% on the test set, outperforming six comparison models. Fixed-site evaluation on 64 independently interpreted image–label pairs from 2014, 2018, 2021, and 2025 produced a pooled mIoU of 76.82% and a Target-mIoU of 70.06% without retraining or threshold adjustment. The ablation results confirmed the contributions of the dual encoder, cross-branch fusion, decoder-side contextual enhancement, and KAN nonlinear mapping. The results demonstrate the potential of DBKNet for regional multi-year mapping, while the reduced accuracy for earlier imagery indicates remaining temporal-transfer limitations.