Convolutional Neural Networks (CNN) have widely used in semantic segmentation, and can effectively extract local hierarchical information while being unsatisfactory in extracting global information. By contrast, Transformer is good at extracting long-distance dependencies in semantics while it is time-consuming. In this work, we propose a Light CNN-Transformer Dual-Branch Network (LCTDBNet) for real-time semantic segmentation. It consists of a longer CNN branch to extract local hierarchical information and a shorter Transformer branch to extract global contextual information. The CNN branch uses a lightweight encoder-decoder structure to further extract more local hierarchical information. We propose a Deep Strip Aggregation Pyramid Pooling Module (DSAPPM) to extract contextual and strip information. We further propose a Feature Pooling Refinement Module (FPRM) to optimise the feature representation at different stages. Finally, we propose a CNN-Transformer Fusion Module (CTFM) to fuse the features of two branches. Experimental results demonstrate that our proposed LCTDBNet is effective and achieves satisfactory results. Specifically, the base version of LCTDBNet achieves 80.3% mean intersection over union (mIoU) at 78.6 frames per second (FPS) on Cityscapes, 80.0% mIoU at 137.5 FPS on CamVid and 40.9% mIoU at 253.7 FPS on ADE20 K.

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the “same object with different spectra” problem is one of the biggest challenges in change detection. To overcome the challenge, a novel unsupervised spatial–spectral feature learning (FL) framework based on style transformation (ST) (called STFL-CD) is proposed for MSI change detection in this article. For dual-temporal MSIs, the proposed STFl-CD algorithm consists of two phases: ST and spatial–spectral FL. Since the image styles are inconsistent under different imaging environments, the first innovation is to transform the image styles through unmixing and reconstruction. Through ST, the challenge of the “same object with different spectra” problem will be reduced fundamentally. By introducing the attention mechanism, the other innovation is to extract the joint spectral–spatial change features based on a 3-D convolutional neural network with spatial and channel attention. In addition, for multitemporal MSIs, a multitemporal version STFL-CD (MT-STFL-CD) framework is designed based on a recurrent neural network to learn the correlation features between multitemporal remote sensing images. Both of the visual and quantitative results on the real MSI datasets indicate that the proposed unsupervised STFL-CD frameworks have significant advantages on multitemporal MSI change detection. In particular, the performance of the proposed unsupervised STFL-CD algorithm is even comparable to that of the state-of-the-art supervised or semisupervised methods.

Effective representation of image texture is important for an image-classification task. Statistical modelling in wavelet domains has been widely used to image texture representation. However, due to the intraclass complexity and interclass diversity of textures, it is hard to use a predefined probability distribution function to fit adaptively all wavelet subband coefficients of different textures. In this article, we propose a novel modelling approach, Heterogeneous and Incrementally Generated Histogram (HIGH), to indirectly model the wavelet coefficients by use of four local features in wavelet subbands. By concatenating all the HIGHs in all wavelet subbands of a texture, we can construct a nonnegative multiresolution vector (NMV) to represent a texture image. Considering the NMV’s high dimensionality and nonnegativity, we further propose a Hessian regularized discriminative nonnegative matrix factorization to compute a low-dimensional basis of the linear subspace of NMVs. Finally, we present a texture classification approach by projecting NMVs on the low-dimensional basis. Experimental results show that our proposed texture classification method outperforms seven representative approaches.