Semantic segmentation is an important and foundational task in the application of high-resolution remote sensing images (HRRSIs). However, HRRSIs feature large differences within categories and minor variances across categories, posing a significant challenge to the high-accuracy semantic segmentation of HRRSIs. To address this issue and obtain powerful feature expressiveness, a deep conditional generative adversarial network (DCGAN), integrating fully convolutional DenseNet (FC-DenseNet) and Pix2pix, is proposed. The DCGAN is composed of a generator–discriminator pair, which is built on a modified downsampling unit of FC-DenseNet. The proposed method possesses strong feature expression ability because of its skip connections, the very deep network structure and multiscale supervision introduced by FC-DenseNet, and the supervision from the discriminator. Experiments on a Deep Globe Land Cover dataset demonstrate the feasibility and effectiveness of this approach for the semantic segmentation of HRRSIs. The results also reveal that our method can mitigate the influence of class imbalance. Our approach for precise semantic segmentation can effectively facilitate the application of HRRSIs.
Dynamic texture (DT) is an extension of texture to the temporal domain. Recognizing DTs has received increasing attention. Volume local binary pattern (VLBP) is the most widely used descriptor for DTs. However, it is time consuming to recognize DTs using VLBP due to the large scale of data and the high dimensionality of the descriptor itself. In this paper, we propose a new operator called orthogonal combination of VLBP (OC-VLBP) for DT recognition. The original VLBP is decomposed both longitudinally and latitudinally, and then combined to constitute the OC-VLBP operator, so that the dimensionality of the original VLBP descriptor is lowered. The experimental results show that the proposed operator significantly reduces the computational costs of recognizing DTs without much loss in recognizing accuracy.
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