The research on sea–land segmentation of remote sensing images has received tremendous attention, which is of great significance to coastline extraction and ocean monitoring. In recent years, various convolutional neural networks (CNNs) have been presented to achieve precise and efficient sea–land segmentation effect. However, existing CNNs typically adopt the symmetric encoder-decoder structure, which is inefficient for feature extraction, feature fusion, and information transmission. To address these problems, this work develops a CNN for pixel-level sea–land segmentation, termed HeteroNet. The proposed HeteroNet constructs a heterogeneous encoder–decoder structure consisting of successive dense-connected encoding modules and squeeze-and-excitation-connected decoding modules that can effectively enhance the feature extraction and fusion capabilities of the network. In addition, an easy-to-embed global context enhanced module is designed to further facilitate information transmission efficiency. Comparative experiments with state-of-the-art methods are conducted to reveal that the HeteroNet can exhibit superior sea–land segmentation performance in different scenarios, and the ablation study is performed to demonstrate the effectiveness of each component in the network.