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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 (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula>) (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.