The power of features considerably influences the classification performance of remote sensing scene classification (RSSC). Recently, deep convolutional neural networks (DCNNs) have been used to extract powerful scene features. Nevertheless, confusion and overlap still occur in the feature space, leading to inaccurate RSSC. To alleviate this problem, we propose a novel deep metric learning loss function incorporated into a sphere loss to enhance the discrimination of feature representations. Inspired by two representative loss functions (i.e., angular loss and center loss), the proposed sphere loss learns a unique cluster center for each class in a remote sensing scene. Because the cluster centers and features are restricted by an introduced geometrical constraint, the intraclass distance of features decreases, while the interclass distance increases. Moreover, we introduce a spatial constraint, i.e., a uniformity coefficient on different cluster centers, which causes the centers to form a uniform distribution that maximizes the interclass distances between features. Extensive analysis and experiments on three commonly used RSSC data sets consistently show that, compared with state-of-the-art methods, the proposed sphere loss can effectively learn discriminative feature representations and significantly improve RSSC.

Automatical inshore ship change detection in optical remote sensing images is meaningful for a wide range of applications, but still a challenging task. In this paper, a visual search inspired model is proposed for inshore ship change detection. The proposed model achieves inshore ship change information extraction based on spatial-temporal saliency detection which mimics visual search mechanism. Experimental results performed on a Google Earth optical remote sensing image data set demonstrate the effectiveness of the proposed model in terms of visual and objective evaluations.

Object detection in complex optical remote sensing images is a challenging problem due to the wide variety of scales, densities, and shapes of object instances on the earth surface. In this letter, we focus on the wide-scale variation problem of multiclass object detection and propose an effective object detection framework in remote sensing images based on YOLOv2. To make the model adaptable to multiscale object detection, we design a network that concatenates feature maps from layers of different depths and adopt a feature introducing strategy based on oriented response dilated convolution. Through this strategy, the performance for small-scale object detection is improved without losing the performance for large-scale object detection. Compared to YOLOv2, the performance of the proposed framework tested in the DOTA (a large-scale data set for object detection in aerial images) data set improves by 4.4% mean average precision without adding extra parameters. The proposed framework achieves real-time detection for$1024\times 1024$image using Titan Xp GPU acceleration.11https://github.com/WenchaoliuMUC/Detection-of-Multiclass-Objects-in-Optical-Remote-Sensing-Images

Region-of-interest (ROI) detection plays a significant role in the analysis and interpretation of remote sensing images (RSI), due to the huge size of satellite images and their explosive growth in quantity. However, when applied to panchromatic RSI directly, traditional saliency models cannot achieve satisfying performance for two reasons: one is the computational efficiency decrease caused by the huge image size; the other is the absence of color information for panchromatic RSI. Thus, in this letter, an ROI detection model based on statistical distinctiveness (SD) is proposed for saliency analysis and ROIs detection in panchromatic RSI. The proposed SD model incorporates both the lower order SD (LSD) and the higher order SD (HSD), in order to identify regions of interest that are highly distinctive from the rest of the scene. Finally, the saliency map is determined by fusing cue maps obtained by calculating LSD locally and HSD globally. Experimental results show that our approach achieves promising results when compared with existing state-of-the-art saliency detection models.

To adequately exploit spectral, spatial, and label information of the given hyperspectral data, a kernel fused representation-based classifier via a spatial-spectral composite kernel with ideal regularization (CKIR) method is proposed in this letter. Specifically, the learned CKIR is embedded into the kernel version of representation-based classifiers, i.e., kernel sparse representation-based classifier (KSRC) and kernel collaborative representation-based classifier (KCRC), to obtain more discriminative representation coefficients. Furthermore, to benefit from both sparsity and data correlation in representation, KSRC and KCRC are combined in the CKIR-based residual domain to further enhance the discriminative ability of the proposed classifier. The experimental results on two real hyperspectral images demonstrate that the proposed method outperforms the other state-of-the-art classifiers.

In this paper, an efficient and accurate multilevel change detection method is presented, achieved by the combination of an unsupervised object-based correlation analysis and a supervised post-classification comparison. Before the change detection procedure, fast multitemporal segmentation is applied to provide object-level information on two registered images. Then the proposed object-based correlation analysis method is used to extract the potential changed areas efficiently and stably, which improves the accuracy of overall performance. Notably, all the procedures are highly automatic except for the necessary selection of training examples within all supervised algorithms. The experimental results demonstrate the superior performance of our method compared with the four typical state-of-the-art change detection methods.

Ship detection is a challenging problem in complex optical remote-sensing images. In this letter, an effective ship detection framework in remote-sensing images based on the convolutional neural network is proposed. The framework is designed to predict bounding box of ship with orientation angle information. Note that the angle information which is added to bounding box regression makes bounding box accurately fit into the ship region. In order to make the model adaptable to the detection of multiscale ship targets, especially small-sized ships, we design the network with feature maps from the layers of different depths. The whole detection pipeline is a single network and achieves real-time detection for a $704 \times 704$ image with the use of Titan X GPU acceleration. Through experiments, we validate the effectiveness, robustness, and accuracy of the proposed ship detection framework in complex remote-sensing scenes.

In recent times, many efforts have been made to improve remote sensing image scene classification, especially using popular deep convolutional neural networks. However, most of these methods do not consider the specific scene orientation of the remote sensing images. In this letter, we propose the improved oriented response network (IORN), which is based on the ORN, to handle the orientation problem in remote sensing image scene classification. We propose average active rotating filters (A-ARFs) in the IORN. While IORNs are being trained, A-ARFs are updated by a method that is different from the ARFs of the ORN, without additional computations. This change helps IORN improve its ability to encode orientation information and speeds up optimization during training. We also propose Squeeze-ORAlign (S-ORAlign) by adding a squeeze layer to ORAlign of ORN. With the squeeze layer, S-ORAlign can address large-scale images, unlike ORAlign. An ablation study and comparison experiments are designed on a public remote sensing image scene classification data set. The experimental results demonstrate the effectiveness and better performance of the proposed model over that of other state-of-the-art models.

Automatic detection of moving targets is one of important research area in the remote sensing field. In this paper, we propose a method that accurately detects moving targets in aerial videos using hierarchical spatiotemporal saliency analysis. First, coarse motion regions are extracted by utilizing global temporal saliency analysis. Based on these local candidate regions, spatial saliency methods are used to obtain accurate description of targets. After fusing spatial and temporal saliency values, we can get refined results of the detection. Considering about the inter-frame consistency of motion, trajectory level analysis is added in the proposed method to eliminate false alarms. Experiments conducted on the VIVID dataset validate the effectiveness and efficiency of the proposed method.

Sea-land segmentation is a key step for some important applications of panchromatic remote sensing image processing. However, robust and effective sea-land segmentation for high-resolution panchromatic remote sensing images is still a challenging problem. This letter presents an accurate and robust approach by integrating the improved multiscale normalized cut (IMNcut) method and improved Chan-Vese model for sea-land segmentation. At first, the image is downsampled and segmented into multiple regions by the IMNcut method. Next, the homogeneous regions are merged to obtain a coarse segmentation result. Finally, gray intensity and local entropy features are integrated as discriminants of the improved Chan-Vese model, which is used to obtain the final segmentation result through a low- to high-resolution segmentation scheme. Experimental results performed on several real data sets demonstrate the effectiveness of the proposed model in terms of visual and objective evaluations.

Traditional region-of-interest (ROI) detection methods for remote sensing images are generally formulated at pixel level and are less efficient when applied on large high-resolution images. This letter presents an accurate and efficient approach via superpixel-to-pixel saliency analysis for ROI detection. At first, the image is downsampled and segmented into superpixels by simple linear iterative clustering. Next, structure tensor and background contrast are used to yield superpixel feature maps for texture and color. After fusing the feature maps, the overall superpixel saliency map is obtained and then used to achieve the final pixel-level saliency map by superpixel-to-pixel mapping. Through experimentations, we validate the effectiveness and computational efficiency of the proposed model in comparison with state-of-the-art techniques.