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Data-driven deep neural networks have demonstrated their superiority in high-resolution remote-sensing image (HRSI) classification based on superpixel-based objects. Currently, most HRSI classification methods that combine deep learning and superpixel object segmentation use multiple scales of stacking to satisfy the contextual semantic-information extraction of one analyzed object. However, this approach does not consider the long-distance dependencies between objects, which not only weakens the representation of feature information but also increases computational redundancy. To solve this problem, a superpixel-based long-range dependent network is proposed for HRSI classification. First, a superpixel segmentation algorithm is used to segment HRSI into homogeneous analysis objects as input. Secondly, a multi-channel deep convolutional neural network is proposed for the feature mapping of the analysis objects. Finally, we design a long-range dependent framework based on a long short-term memory (LSTM) network for obtaining contextual relationships and outputting classes of analysis objects. Additionally, we define the semantic range and investigate how it affects classification accuracy. A test is conducted by using two HRSI with overall accuracy (0.79, 0.76) and kappa coefficients (κ) (0.92, 0.89). Both qualitative and quantitative comparisons are adopted to test the proposed method’s efficacy. Findings concluded that the proposed method is competitive and consistently superior to the benchmark comparison method.