This letter proposes a simple but effective approach to automatically learn a multilayer image feature for satellite image scene classification. Different from the hand-crafted features which are ...empirically designed but lack high generalization ability, the proposed approach can autonomously extract the data-dependent feature. The presented feature extraction algorithm is composed of two layers, and the bases of these two layers are uniformly learned by a plain K-means clustering algorithm. Coincidentally, the feature extraction performance of the aforementioned two layers is consistent with visual processing of human visual cortex. More specifically, the first layer can generate edgelike bases, which are analogous to the neuron responses of primary visual cortex (V1), and the second layer can produce cornerlike bases, which resemble the neuron responses of visual extrastriate cortical area two (V2). The proposed feature extraction approach can automatically extract not only simple structure features (e.g., edges) but also complex structure features (e.g., corners and junctions). The learned feature is further discriminated by the linear support vector machine classifier for scene classification. In order to fairly demonstrate the validity of the proposed feature extraction approach, its satellite image scene classification performance is evaluated on the public UCM-21 data set. Experimental results show that the proposed approach can outperform several recent state-of-the-art approaches.
Deep learning-based synthetic aperture radar (SAR) image classification is an open problem when training samples are scarce. Transfer learning-based few-shot methods are effective to deal with this ...problem by transferring knowledge from the electro-optical (EO) to the SAR domain. The performance of such methods relies on extra SAR samples, such as unlabeled novel class's samples or labeled similar classes samples. However, it is unrealistic to collect sufficient extra SAR samples in some application scenarios, namely the extreme few-shot case. In this case, the performance of such methods degrades seriously. Therefore, few-shot methods that reduce the dependence on extra SAR samples are critical. Motivated by this, a novel few-shot transfer learning method for SAR image classification in the extreme few-shot case is proposed. We propose the connection-free attention module to selectively transfer features shared between EO and SAR samples from a source network to a target network to supplement the loss of information brought by extra SAR samples. Based on the Bayesian convolutional neural network, we propose a training strategy for the extreme few-shot case, which focuses on updating important parameters, namely the accurately updating important parameters. The experimental results on the three real-SAR datasets demonstrate the superiority of our method.
Compute-and-forward (CF) harnesses interference in a wireless network by allowing relays to compute combinations of source messages. The computed message combinations at relays are correlated, and so ...directly forwarding these combinations to a destination generally incurs information redundancy and spectrum inefficiency. To address this issue, we propose a novel relay strategy, termed compute-compress-and-forward (CCF). In CCF, source messages are encoded using nested lattice codes constructed on a chain of nested coding and shaping lattices. A key difference of CCF from CF is an extra compressing stage inserted in between the computing and forwarding stages of a relay, so as to reduce the forwarding information rate of the relay. The compressing stage at each relay consists of two operations: first to quantize the computed message combination on an appropriately chosen lattice (referred to as a quantization lattice), and then to take modulo on another lattice (referred to as a modulo lattice). We study the design of the quantization and modulo lattices and propose successive recovering algorithms to ensure the recoverability of source messages at destination. Based on that, we formulate a sum-rate maximization problem that is in general an NP-hard mixed integer program. A low-complexity algorithm is proposed to give a suboptimal solution. Numerical results are presented to demonstrate the superiority of CCF over the existing CF schemes.
This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient ...textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.
This letter presents a new method for airport detection from large high-spatial-resolution IKONOS images. To this end, we describe airport by a set of scale-invariant feature transform (SIFT) ...keypoints and detect it using an improved SIFT matching strategy. After obtaining SIFT matched keypoints, to both discard the redundant matched points and locate the possible regions of candidates that contain the target, a novel region-location algorithm is proposed, which exploits the clustering information from matched SIFT keypoints, as well as the region information extracted through the image segmentation. Finally, airport recognition is achieved by applying the prior knowledge to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.
Fine-grained target recognition in Synthetic Aperture Radar(SAR) or infrared imaging modal is an open problem in some application scenarios where training samples are scarce.Transferring common ...features from Visible Optical(VO) samples is effective for the case that SAR (infrared) samples are scarce. However, for the fine-grained target recognition, transferring common features face two issues: 1) common features can be divided into fine-grained features and the coarse-grained features. For the fine-grained target recognition task in the few-shot case, how to transfer fine-grained common features needed to be considered; 2) in the SAR (infrared) imaging modal, parts of samples carry much noise because of the limitation of the imaging mechanism, masking the subtle difference for the fine-grained target recognition task, making such fine-grained common features not easy to be transferred, especially when training samples are scarce. To handle these issues, corresponding solutions are proposed in this paper as follows: 1) the CF-contrastive loss is proposed to transfer fine-grained common features from VO samples; 2) based on the modeling of the heteroscedastic uncertainty, the training strategy of sample quality evaluation (SQE) is proposed to emphasize the training samples with less noise. Experiments on three datasets, including MSTAR, P-openSARship, and P-VAIS represent the superiority of the proposed algorithm over baseline and other popular CDFSL algorithms.
Tracking multiple pedestrians across multi-camera scenarios is an important part of intelligent video surveillance and has great potential application for public security, which has been an ...attractive topic in the literature in recent years. In most previous methods, artificial features such as color histograms, HOG descriptors and Haar-like feature were adopted to associate objects among different cameras. But there are still many challenges caused by low resolution, variation of illumination, complex background and posture change. In this paper, a feature extraction network named NCA-Net is designed to improve the performance of multiple objects tracking across multiple cameras by avoiding the problem of insufficient robustness caused by hand-crafted features. The network combines features learning and metric learning via a Convolutional Neural Network (CNN) model and the loss function similar to neighborhood components analysis (NCA). The loss function is adapted from the probability loss of NCA aiming at object tracking. The experiments conducted on the NLPR_MCT dataset show that we obtain satisfactory results even with a simple matching operation. In addition, we embed the proposed NCA-Net with two existing tracking systems. The experimental results on the corresponding datasets demonstrate that the extracted features using NCA-net can effectively make improvement on the tracking performance.
Building structural type (BST) information is vital for seismic risk and vulnerability modeling. However, obtaining this kind of information is not a trivial task. The conventional method involves a ...labor-intensive and inefficient manual inspection process for each building. Nowadays, a few methods have explored to use remote sensing images and some building-related knowledge (BRK) to realize automated BST recognition. However, these methods have many limitations, such as insufficient mining of multimodal information and difficulty obtaining BRK, which hinders their promotion and practical use. To alleviate the shortcomings above, we propose a deep multimodal fusion model, which combines satellite optical remote sensing image, aerial synthetic aperture radar image, and BRK (roof type, color, and group pattern) obtained by domain experts to achieve accurate automatic reasoning of BSTs. Specifically, first, we use a pseudo-siamese network to extract the image feature. Second, a knowledge graph (KG) based on the BRK is constructed, and then, we use a graph attention network to extract the semantic feature from the KG. Third, we propose a novel multistage gated fusion mechanism to fuse the image and semantic feature. Our method's best overall accuracy and kappa coefficient on the dataset collected in the study area are 90.35% and 0.83, which outperforms a series of existing methods. Through our model, high-precision BST information can be obtained for earthquake disaster prevention, reduction, and emergency decision making.
Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of ...natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.
Given a set of high-resolution remote sensing images covering different scenes, we propose an unsupervised approach to simultaneously detect possible built-up areas from them. The motivation behind ...is that the frequently recurring appearance patterns or repeated textures corresponding to common objects of interest (e.g., built-up areas) in the input image data set can help us discriminate built-up areas from others. With this inspiration, our method consists of two steps. First, we extract a large set of corners from each input image by an improved Harris corner detector. Afterward, we incorporate the extracted corners into a likelihood function to locate candidate regions in each input image. Given a set of candidate build-up regions, in the second stage, we formulate the problem of build-up area detection as an unsupervised grouping problem. The candidate regions are modeled through texture histogram, and the grouping problem is solved by spectrum clustering and graph cuts. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.
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