An Improved DeepLabV3+ Network for Bare Rock Segmentation in Remote Sensing Images

To improve the binary semantic segmentation of bare rock and background in complex urban environments, this study developed an improved DeepLabV3+ model for red–green–blue (RGB) imagery derived from Gaofen-1 (GF-1) data. Four backbone networks—Xception, ResNet50, MobileNetV2, and MobileNetV3—were first evaluated. ResNet50 was selected because it achieved the highest bare-rock IoU and bare-rock F1-score among the evaluated backbones under the evaluated experimental configuration. An Adam-based training configuration was subsequently evaluated against a stochastic gradient descent (SGD)-based configuration and selected for the ResNet50 baseline. Squeeze-and-excitation (SE) channel attention, a spatial attention module (SATM), and a receptive field block (RFB) were then integrated to enhance feature representations from channel, spatial, and multi-scale perspectives. In the repeated evaluation of the proposed model within the ablation experiment using three independent training runs, the model achieved a mean bare-rock IoU of 79.36 ± 0.30%, a mean bare-rock precision of 88.65 ± 0.55%, and a mean bare-rock F1-score of 88.49 ± 0.19%. These results indicate favorable overall bare-rock segmentation performance under the evaluated experimental setting. Compared with the original DeepLabV3+ configuration (Xception + SGD), the proposed model achieved improvements of 14.51, 4.80, and 9.82 percentage points in bare-rock IoU, precision, and F1-score, respectively. These improvements reflect the combined effects of backbone selection, optimizer configuration, and the proposed module integration. Compared with the ResNet50 + Adam baseline model established in this study, the proposed model further improved these metrics by 5.19, 4.60, and 3.32 percentage points, respectively. Among the evaluated models, the proposed method achieved the highest bare-rock IoU and bare-rock F1-score, whereas U-Net achieved a higher bare-rock precision. Thus, the comparative advantage of the proposed model was primarily reflected in segmentation overlap and overall F1 performance rather than in precision. Qualitative comparisons further showed favorable boundary continuity and feature discrimination in representative complex urban scenes containing buildings, vegetation, roads, and shadow-affected areas. These results provide methodological support for bare-rock segmentation and related geological remote-sensing applications in complex plateau urban environments.