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Integrating superpixel segmentation into convolutional neural networks is known to be effective in enhancing the accuracy of land-cover classification. However, most of existing methods accomplish such integration by focusing on the development of new network architectures, which suffer from several flaws: conflicts between general superpixels and semantic labels introduce noise into the training, especially at object boundaries; absence of training guidance for superpixels leads to ineffective regional feature learning; and unnecessary superpixel segmentation in the testing stage not only increases the computational burden but also incurs jagged edges. In this study, we propose a novel semantic-aware region (SARI) loss to guide the effective learning of regional features with superpixels for accurate land-cover classification. The key idea of the proposed method is to reduce the feature variance inside and between homogeneous superpixels while enlarging feature discrepancy between heterogeneous ones. The SARI loss is thus designed with three subparts, including superpixel variance loss, intraclass similarity loss and interclass distance loss. We also develop semantic superpixels to assist in the network training with SARI loss while overcoming the limitations of general superpixels. Extensive experiments on two challenging datasets demonstrate that the SARI loss can facilitate regional feature learning, achieving state-of-the-art performance with mIoU scores of around 97.11% and 73.99% on Gaofen Image dataset and DeepGlobe dataset, respectively.

Neural style transfer recently has become one of the most popular topics in academic research and industrial application. The existing methods can generate synthetic images by transferring different styles of some images to another given content images, but they mainly focus on learning low-level features of images with losses of content and style, leading to greatly alter the salient information of content images in the semantic level. In this paper, an improved scheme is proposed to keep the salient regions of the transferred image the same with that of content image. By adding the region loss calculated from a localization network, the synthetic image can almost keep the main salient regions consistent with that of original content image, which helps for saliency-based tasks such as object localization and classification. In addition, the transferred effect is more natural and attractive, avoiding simple texture overlay of the style image. Furthermore, our scheme can also extend to remain other semantic information (such as shape, edge, and color) of the image with the corresponding estimation networks.