With the rapid development of various technologies of satellite sensor, synthetic aperture radar (SAR) image has been an import source of data in the application of change detection. In this paper, a novel method based on a convolutional neural network (CNN) for SAR image change detection is proposed. The main idea of our method is to generate the classification results directly from the original two SAR images through a CNN without any preprocessing operations, which also eliminate the process of generating the difference image (DI), thus reducing the influence of the DI on the final classification result. In CNN, the spatial characteristics of the raw image can be extracted and captured by automatic learning and the results with stronger robustness can be obtained. The basic idea of the proposed method includes three steps: it first produces false labels through unsupervised spatial fuzzy clustering. Then we train the CNN through proper samples that are selected from the samples with false labels. Finally, the final detection results are obtained by the trained convolutional network. Although training the convolutional network is a supervised learning fashion, the whole process of the algorithm is an unsupervised process without priori knowledge. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of our algorithm in simulated and real data sets. In addition, we try to apply our algorithm to the change detection of heterogeneous images, which also achieves satisfactory results.

Object detection has made significant progress in many real-world scenes. Despite this remarkable progress, the common use case of detection in remote sensing images remains challenging even for leading object detectors, due to the complex background, objects with arbitrary orientation, and large difference in scale of objects. In this paper, we propose a novel rotation detector for remote sensing images, mainly inspired by Mask R-CNN, namely RADet. RADet can obtain the rotation bounding box of objects with shape mask predicted by the mask branch, which is a novel, simple and effective way to get the rotation bounding box of objects. Specifically, a refine feature pyramid network is devised with an improved building block constructing top-down feature maps, to solve the problem of large difference in scales. Meanwhile, the position attention network and the channel attention network are jointly explored by modeling the spatial position dependence between global pixels and highlighting the object feature, for detecting small object surrounded by complex background. Extensive experiments on two remote sensing public datasets, DOTA and NWPUVHR -10, show our method to outperform existing leading object detectors in remote sensing field.

Automatic and accurate semantic segmentation from high-resolution remote-sensing images plays an important role in the field of aerial images analysis. The task of dense semantic segmentation requires that semantic labels be assigned to each pixel in the image. Recently, convolutional neural networks (CNNs) have proven to be powerful tools for image classification, and they have been adopted in the remote-sensing community. But many limitations still exist when modern CNN architectures are directly applied to remote-sensing images, such as gradient explosion when the depth of the network increases, over-fitting with limited labeled remote-sensing data, and special differences between remote-sensing images and natural images. In this paper, we present a novel architecture that combines the thought of dense connection and fully convolutional networks, referred as DFCN, to automatically provide fine-grained semantic segmentation maps. In addition, we improve DFCN with multi-scale filters to widen the network and to increase the richness and diversity of extracted information, making the network more powerful and expressive than the naive convolution layer. Furthermore, we investigate a multi-modal network that incorporates digital surface models (DSMs) into a DFCN structure, and then we propose dual-path densely convolutional networks where the encoder consists of two paths that, respectively, extract features from spectral data and DSMs data and then fuse them. Finally, through conducting comprehensive experimental evaluations on two remote sensing benchmark datasets, we test our proposed models and compare them with other deep networks. The results demonstrate the effectiveness of proposed approaches; they can achieve competitive performance compared with the current state-of-the-art methods.

Feature selection is an important approach for reducing the dimension of high-dimensional data. In recent years, many feature selection algorithms have been proposed, but most of them only exploit information from the data space. They often neglect useful information contained in the feature space, and do not make full use of the characteristics of the data. To overcome this problem, this paper proposes a new unsupervised feature selection algorithm, called non-negative spectral learning and sparse regression-based dual-graph regularized feature selection (NSSRD). NSSRD is based on the feature selection framework of joint embedding learning and sparse regression, but extends this framework by introducing the feature graph. By using low dimensional embedding learning in both data space and feature space, NSSRD simultaneously exploits the geometric information of both spaces. Second, the algorithm uses non-negative constraints to constrain the low-dimensional embedding matrix of both feature space and data space, ensuring that the elements in the matrix are non-negative. Third, NSSRD unifies the embedding matrix of the feature space and the sparse transformation matrix. To ensure the sparsity of the feature array, the sparse transformation matrix is constrained using the L _2,1 -norm. Thus feature selection can obtain accurate discriminative information from these matrices. Finally, NSSRD uses an iterative and alternative updating rule to optimize the objective function, enabling it to select the representative features more quickly and efficiently. This paper explains the objective function, the iterative updating rules and a proof of convergence. Experimental results show that NSSRD is significantly more effective than several other feature selection algorithms from the literature, on a variety of test data.

The advent of the sixth generation of mobile communications (6G) ushers in an era of heightened demand for advanced network intelligence to tackle the challenges of an expanding network landscape and increasing service demands. Deep Learning (DL), as a crucial technique for instilling intelligence into 6G, has demonstrated powerful and promising development. This paper provides a comprehensive overview of the pivotal role of DL in 6G, exploring the myriad opportunities and challenges that arise. Firstly, we present a detailed vision for DL in 6G, emphasizing areas such as adaptive resource allocation, intelligent network management, robust signal processing, ubiquitous edge intelligence, and endogenous security. Secondly, this paper reviews how DL models leverage their unique learning capabilities to solve complex service demands in 6G. The models discussed include Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Graph Neural Networks (GNN), Deep Reinforcement Learning (DRL), Transformer, Federated Learning (FL), and Meta Learning. Additionally, we examine the specific challenges each DL model faces within the 6G context. Moreover, we delve into the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), examining its development and impact within the 6G framework. Finally, this paper culminates in a detailed discussion of ten critical open problems in integrating DL with 6G, setting the stage for future research and development in this field.

In recent years, despite the tremendous progresses of object detection, small object detection has always been a challenge in the field of remote sensing. The main reason is that small objects contain few features which are easily lost during down-sampling. In this paper, we propose a cross-layer attention network (CANet) aiming to obtain stronger features of small objects for better detection. Specifically, we designed an up-sampling and down-sampling feature pyramid (U-DFP) to obtain richer context information by bidirectionally fusing deep and shallow features, as well as skipping connections. Moreover, a cross-layer attention module (CAM) is designed to obtain the non-local association of small objects in each layer, and further strengthen its representation ability through cross-layer integration and balance. Extensive experiments on the publicly available dataset (DIOR dataset and NWPUVHR-10 dataset) and the self-assembled dataset (SDOTA dataset and SDD dataset) show the excellent performance of our method compared with other detectors. Moreover, our method achieved 74.3% mAP on the public DIOR dataset without any tricks.