The objective of detection in remote sensing images is to determine the location and category of all targets in these images. The anchor based methods are the most prevalent deep learning based methods, and still have some problems that need to be addressed. First, the existing metric (i.e., intersection over union (IoU)) could not measure the distance between two bounding boxes when they are nonoverlapping. Second, the exsiting bounding box regression loss could not directly optimize the metric in the training process. Third, the existing methods which adopt a hierarchical deep network only choose a single level feature layer for the feature extraction of region proposals, meaning they do not take full use of the advantage of multi-level features. To resolve the above problems, a novel object detection method for remote sensing images based on improved bounding box regression and multi-level features fusion is proposed in this paper. First, a new metric named generalized IoU is applied, which can quantify the distance between two bounding boxes, regardless of whether they are overlapping or not. Second, a novel bounding box regression loss is proposed, which can not only optimize the new metric (i.e., generalized IoU) directly but also overcome the problem that existing bounding box regression loss based on the new metric cannot adaptively change the gradient based on the metric value. Finally, a multi-level features fusion module is proposed and incorporated into the existing hierarchical deep network, which can make full use of the multi-level features for each region proposal. The quantitative comparisons between the proposed method and baseline method on the large scale dataset DIOR demonstrate that incorporating the proposed bounding box regression loss, multi-level features fusion module, and a combination of both into the baseline method can obtain an absolute gain of 0.7%, 1.4%, and 2.2% or so in terms of mAP, respectively. Comparing this with the state-of-the-art methods demonstrates that the proposed method has achieved a state-of-the-art performance. The curves of average precision with different thresholds show that the advantage of the proposed method is more evident when the threshold of generalized IoU (or IoU) is relatively high, which means that the proposed method can improve the precision of object localization. Similar conclusions can be obtained on a NWPU VHR-10 dataset.

Oriented object detection (OOD) aims to precisely detect the objects with arbitrary orientation in remote sensing images. Up to now, most of bounding box regression (BBR) losses for OOD are transferred from horizontal object detection (HOD) methods, however, the transferring requires lots of professional knowledge and experiences for designers, consequently, many excellent BBR losses for HOD have not been transferred to OOD. To accelerate the research progress of BBR loss for OOD, a unified transferring strategy (UTS) is proposed to facilitate the transferring of BBR loss from HOD to OOD. The UTS proposes that the BBR of oriented bounding box (OBB) can be converted into the joint BBR of its horizontal smallest enclosing rectangle (HSER) and two offsets, so the BBR loss in HOD can be easily transferred to OOD by using HSER as a bridge. Following the UTS, a BBR loss named Rotated-IoU (RIoU) loss is designed for OOD by transferring an advanced BBR loss in HOD, which can be considered as an example to show how to transfer. On the basis of RIoU loss, a focal rotated-IoU (FRIoU) loss is proposed to assign larger weights to hard samples in the BBR. The comparisons with other BBR losses show that the RIoU and FRIoU losses can give better performance. The ablation study shows that giving more attention to hard samples in BBR is effective. The comparisons with many advanced methods demonstrate that the combinations of baseline methods and FRIoU loss achieve state-of-the-art performance on the DOTA and DIOR-R datasets.

In the paper, a novel color image encryption scheme is proposed by use of the three-dimensional chaotic maps and some data reconstruction techniques. In the diffusion process of the encryption algorithm, the three-dimensional chaotic Logistic map is introduced to changes the pixel value of the plain image. Meanwhile, in the confusion process, the three-dimensional chaotic Cat map is applied to deal with the position of the image pixels. Moreover, some data reconstruction techniques are employed for the better encryption result. Furthermore, the corresponding decryption algorithm is also given. The significant advantages of our method are the effectiveness and simplicity, and our proposed method breaks the correlation between image pixel layers, offers a large key space and against differential and statistical attacks effectively. Finally, the superiority of the proposed algorithm will be shown by the experimental results, security analysis, and the corresponding comparisons with literature.

The detection results of many existing co-saliency detection methods are easily interfered by the unrelated salient objects, which have similar appearance characteristics to co-salient objects. Therefore, mining the inter-saliency cues which contain the common category information of multiple related images is the core of co-saliency detection. To address above concern, a novel group weakly supervised learning induced co-saliency detection (GWSCoSal) model is proposed in this paper. First of all, a novel group class activation maps (GCAM) network is constructed and trained through a group weakly supervised learning scheme, which adopts the common category of a group of related images as the ground truth. The GCAM produced by the trained GCAM network are considered as the inter-saliency cues, which can only highlight the regions covered by the objects with common category. Afterwards, the GCAM are integrated into a feature pyramid networks (FPN) based backbone trained by the pixel-level labels to infer the co-saliency maps. The group weakly supervised and the pixel-level learning are jointly implemented for end-to-end training of GWSCoSal model. The comprehensive comparisons with 13 state-of-the-art methods demonstrate that, our GWSCoSal model can detect the co-salient objects more accurately under the condition of being interfered by the similar unrelated salient objects, and the overall performance of which has achieved the level of state-of-the-art methods. The ablation study of our GWSCoSal model validates the effectiveness of proposed GCAM network.

Weakly supervised object detection in remote sensing image (RSI) is still a challenge because of the lack of instance-level labels, and many existing methods have two problems. Firstly, most of the existing methods usually mine the pseudo ground truth (PGT) instances solely relying on proposal class scores (PCS). Actually, the reliability of PCS is not enough because of the bird’s eye view imaging and large-scale chaotic background of RSIs, and the instances with high PCS incline to cover the discriminative region rather than the whole object. Secondly, the existing methods assign a one-hot label to each instance, and the label of PGT instance is copied to its neighbor instances, which induces the misclassification problem to some extent. Actually, the probability that the neighbor instances contain the object with the same category is smaller than the PGT instance. For the first problem, the proposal quality score (PQS) is proposed for mining high-quality PGT instances, which contains PCS and dual-context projection score (DCPS). The DCPS is calculated through semantic segmentation, and is employed to measure the completeness that each proposal covers an object. For the second problem, a pseudo soft label assignment (PSLA) strategy is proposed to assign more precise soft label for each instance, where the soft label is determined by the spatial distance between each instance and its nearest PGT instance. The ablation study validates the effectiveness of the PQS and PSLA. The comprehensive comparisons with other WSOD methods on three popular benchmarks show the excellent performance of our method.

Oriented object detection (OOD) can more accurately locate objects with an arbitrary direction in remote sensing images (RSIs) compared to horizontal object detection. The most commonly used bounding box regression (BBR) loss in OOD is smooth L1 loss, which requires the precondition that spatial parameters are independent of one another. This independence is an ideal that is not achievable in practice. To avoid this problem, various kinds of IoU-based BBR losses have been widely used in OOD; however, their relationships with IoUs are approximately linear. Consequently, the gradient value, i.e., the learning intensity, cannot be dynamically adjusted with the IoU in these cases, which restricts the accuracy of object location. To handle this problem, a novel BBR loss, named smooth generalized intersection over union (GIoU) loss, is proposed. The contributions it makes include two aspects. First of all, smooth GIoU loss can employ more appropriate learning intensities in the different ranges of GIoU values to address the above problem and the design scheme of smooth GIoU loss can be generalized to other IoU-based BBR losses. Secondly, the existing computational scheme of GIoU loss can be modified to fit OOD. The ablation study of smooth GIoU loss validates the effectiveness of its design scheme. Comprehensive comparisons performed on two RSI datasets demonstrate that the proposed smooth GIoU loss is superior to other BBR losses adopted by existing OOD methods and can be generalized for various kinds of OOD methods. Furthermore, the core idea of smooth GIoU loss can be generalized to other IoU-based BBR losses.