Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.
In this paper, a component-based multi-layer parallel network is proposed for airplane detection in Synthetic Aperture Radar (SAR) imagery. In response to the problems called sparsity and diversity brought by SAR scattering mechanism, depth characteristics and component structure are utilized in the presented algorithm. Compared with traditional features, the depth characteristics have better description ability to deal with diversity. Component information is contributing in detecting complete targets. The proposed algorithm consists of two parallel networks and a constraint layer. First, the component information is introduced into the network by labeling. Then, the overall target and corresponding components are detected by the trained model. In the following discriminative constraint layer, the maximum probability and prior information are adopted to filter out wrong detection. Experiments for several comparative methods are conducted on TerraSAR-X SAR imagery; the results indicate that the proposed network has a higher accuracy for airplane detection.
Automatic image registration of optical-to-Synthetic aperture radar (SAR) images is difficult because of the inconsistency of radiometric and geometric properties between the optical image and the SAR image. The intensity-based methods may require many calculations and be ineffective when there are geometric distortions between these two images. The feature-based methods have high requirements on features, and there are certain challenges in feature extraction and matching. A new automatic optical-to-SAR image registration framework is proposed in this paper. First, modified holistically nested edge detection is employed to detect the main contours in both the optical and SAR images. Second, a mesh grid strategy is presented to perform a coarse-to-fine registration. The coarse registration calculates the feature matching and summarizes the preliminary results for the fine registration process. Finally, moving direct linear transformation is introduced to perform a homography warp to alleviate parallax. The experimental results show the effectiveness and accuracy of our proposed method. However, due to the different geometric and radiometric properties of SAR and optical images, to automatically register these two types of images, one must overcome many difficulties. In particular, optical images and SAR images have different geometrical characteristics. Whereas geometric distortions such as foreshortening and layover exist in SAR images, perspective and shadow exist in optical images, which cause the differences between the two types of images. In addition, optical images and SAR images have different radiometric distortion, the SAR sensor is an active remote sensing system, but the optical sensor is a passive system [4]. A large quantity of speckle noise in SAR images renders it difficult to obtain common features from a SAR image and an optical image [5]. For these reasons, the registration of optical images and SAR images has more challenges than mono-sensor image registration.The existing optical-to-SAR registration methods are mainly divided into two types: intensity-based registration methods and feature-based registration methods. Intensity-based registration methods include mutual information (MI) [6], cross-cumulative residual entropy [7] and normalized cross-correlation (NCC) [8]. Although this kind of registration method can register multi-sensor images with intensity differences, it is insensitive to the local differences between the two images and it requires many calculations [9]. Therefore, some improved intensity-based registration methods combined edges and gradient have been proposed [10][11][12]. For example, Cheah et al. [10] proposed the adaptation of MI measure which incorporates the spatial information by combining intensity and gradient information. Chen et al. [13] implemented MI through joint histogram estimation using various interpolation algorithms to complete multi-sensor and multiresolution image registration. Saidi et al. [14] proposed a refined automatic co-registration method (RA...
Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.
Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three key factors of this algorithm are as follows. First, the network combines generative adversarial network and Bayesian framework to realize the estimation from the prior probability to the posterior probability. Second, the skip connected encoder-decoder network is combined with CRF layer to implement end-to-end network training. Finally, the adversarial loss and the cross-entropy loss guide the training of the segmentation network through back propagation. The experimental results show that our proposed method outperformed FCN in terms of mIoU for 0.0342 and 0.11 on two data sets, respectively.
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