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.

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.

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.

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification.

Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse auto encoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification.