Since DCNNs (deep convolutional neural networks) have been successfully applied to various academic and industrial fields, semantic segmentation methods, based on DCNNs, are increasingly explored for remote-sensing image interpreting and information extracting. It is still highly challenging due to the presence of irregular target shapes, and similarities of inter-and intra-class objects in largescale high-resolution satellite images. A majority of existing methods fuse the multi-scale features that always fail to provide satisfactory results. In this paper, a dual attention deep fusion semantic segmentation network of large-scale satellite remote-sensing images is proposed (DASSN_RSI). The framework consists of novel encoderdecoder architecture, and a weight-adaptive loss function based on focal loss. To refine high-level semantic and low-level spatial feature maps, the deep layer channel attention module (DLCAM) and shallow layer spatial attention module (SLSAM) are designed and appended with specific blocks. Then the DUpsampling is incorporated to fuse feature maps in a lossless way. Peculiarly, the weight-adaptive focal loss (W-AFL) is inferred and embedded successfully, alleviating the class-imbalanced issue as much as possible. The extensive experiments are conducted on Gaofen image dataset (GID) datasets (Gaofen-2 satellite images, coarse set with five categories and refined set with fifteen categories). And the results show that our approach achieves state-of-the-art performance compared to other typical variants of encoder-decoder networks in the numerical evaluation and visual inspection. Besides, the necessary ablation studies are carried out for a comprehensive evaluation.

The accurate extraction of rivers is closely related to agriculture, socio-economic, environment, and ecology. It helps us to pre-warn serious natural disasters such as floods, which leads to massive losses of life and property. With the development and popularization of remote-sensing and information technologies, a great number of river-extraction methods have been proposed. However, most of them are vulnerable to noise interference and perform inefficient in a big data environment. To address these problems, a river extraction method is proposed based on adaptive mutation particle swarm optimization (PSO) support vector machine (AMPSO-SVM). First, three features, the spectral information, normalized difference water index (NDWI), and spatial texture entropy, are considered in feature space construction. It makes the objects with the same spectrum more distinguishable, then the noise interference could be resisted effectively. Second, in order to address the problems of premature convergence and inefficient iteration, a mutation operator is introduced to the PSO algorithm. This processing makes transductive SVM obtain optimal parameters quickly and effectively. The experiments are conducted on GaoFen-1 multispectral remote-sensing images from Yellow River. The results show that the proposed method performs better than the existed ones, including PCA, KNN, basic SVM, and PSO-SVM, in terms of overall accuracy and the kappa coefficient. Besides, the proposed method achieves convergence rate faster than the PSO-SVM method.

Pan-sharpening is a significant task that aims to generate high spectral-and spatial-resolution remote-sensing image by fusing multi-spectral (MS) and panchromatic (PAN) image. The conventional approaches are insufficient to protect the fidelity both in spectral and spatial domains. Inspired by the robust capability and outstanding performance of convolutional neural networks (CNN) in natural image super-resolution tasks, CNN-based pan-sharpening methods are worthy of further exploration. In this paper, a novel pan-sharpening method is proposed by introducing a multi-scale channel attention residual network (MSCARN), which can represent features accurately and reconstruct a pan-sharpened image comprehensively. In MSCARN, the multi-scale feature extraction blocks comprehensively extract the coarse structures and high-frequency details. Moreover, the multi-residual architecture guarantees the consistency of feature learning procedure and accelerates convergence. Specifically, we introduce a channel attention mechanism to recalibrate the channel-wise features by considering interdependencies among channels adaptively. The extensive experiments are implemented on two real-datasets from GaoFen series satellites. And the results show that the proposed method performs better than the existing methods both in full-reference and no-reference metrics, meanwhile, the visual inspection displays in accordance with the quantitative metrics. Besides, in comparison with pan-sharpening by convolutional neural networks (PNN), the proposed method achieves faster convergence rate and lower loss.

In remotely sensed images, high intra-class variance and inter-class similarity are ubiquitous due to complex scenes and objects with multivariate features, making semantic segmentation a challenging task. Deep convolutional neural networks can solve this problem by modelling the context of features and improving their discriminability. However, current learning paradigms model the feature affinity in spatial dimension and channel dimension separately and then fuse them in a sequential or parallel manner, leading to suboptimal performance. In this study, we first analyze this problem practically and summarize it as attention bias that reduces the capability of network in distinguishing weak and discretely distributed objects from widerange objects with internal connectivity, when modeled only in spatial or channel domain. To jointly model both spatial and channel affinity, we design a synergistic attention module (SAM), which allows for channel-wise affinity extraction while preserving spatial details. In addition, we propose a synergistic attention perception neural network (SAPNet) for the semantic segmentation of remote sensing images. The hierarchicalembedded synergistic attention perception module aggregates SAM-refined features and decoded features. As a result, SAPNet enriches inference clues with desired spatial and channel details. Experiments on three benchmark datasets show that SAPNet is competitive in accuracy and adaptability compared with stateof-the-art methods. The experiments also validate the hypothesis of attention bias and the efficiency of SAM.

Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in recent years have transferred the DCNN-based model to remote sensing data analysis. However, the wide-range observation areas, complex and diverse objects and illumination and imaging angle influence the pixels easily confused, leading to undesirable results. Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions. HCANet hybridizes cross-level contextual and attentive representations to emphasize the distinguishability of learned features. First of all, a cross-level contextual representation module (CCRM) is devised to exploit and harness the superpixel contextual information. Moreover, a hybrid representation enhancement module (HREM) is designed to fuse cross-level contextual and self-attentive representations flexibly. Furthermore, the decoder incorporates DUpsampling operation to boost the efficiency losslessly. The extensive experiments are implemented on the Vaihingen and Potsdam benchmarks. In addition, the results indicate that HCANet achieves excellent performance on overall accuracy and mean intersection over union. In addition, the ablation study further verifies the superiority of CCRM.