Reliable multimodal image analysis is challenging when complementary sensors exhibit class-dependent strengths and local prediction uncertainty. This study presents a reliability-aware hybrid decision-fusion framework for fine-grained tidal-flat land-cover mapping as a demanding application of multisource image processing. A pyramid scene parsing network (PSPNet) with a 50-layer residual network (ResNet-50) backbone extracts multi-scale spatial context from high-resolution multispectral patches, while a Random Forest models 166-dimensional hyperspectral reflectance signatures. Instead of applying uniform score averaging, the framework combines validation-derived class reliability, pixel-level confidence, and branch disagreement to preserve trusted spatial predictions and selectively introduce spectral correction in uncertain regions; morphological refinement improves local mask continuity. Experiments used GF-2, GF-7, ZY1-02E/F, and ground-reference data acquired from 2022 to 2024, comprising 23 classes, 12,666 image samples, and 452,156 spectral samples. The method achieved 94.64% overall accuracy, 92.85% mean class recall (MCR), 85.47% mean intersection over union (mIoU), 91.72% macro-averaged F1 score (Macro-F1), and 85.88% Small-object F1. Relative to the PSPNet and Random Forest branches, mIoU increased by 4.73 and 3.84 percentage points, while Small-object F1 increased by 5.36 and 6.92 points. The results demonstrate that reliability-aware hybrid fusion can improve both spectrally ambiguous surface classification and spatially fragmented object recognition in complex multimodal scenes.