To address the problem of imbalanced datasets in SAR ship classification, this paper presents a novel cosine affinity (CA) loss that enhances the Gaussian affinity (GA) loss. The CA loss focuses on the angular relationship between feature vectors, prioritizing their direction over their magnitude, which is advantageous for high-dimensional space analysis. In addition, class weights are incorporated to compute weighted distances. Importantly, the proposed CA loss does not increase the computational complexity of algorithm, nor does it lead to overfitting problems associated with data-level techniques. Through various experiments, its effectiveness has been demonstrated by achieving the highest F1 score and recall compared to other existing loss functions, highlighting its superior ability to classify minority classes in FUSARShip.

In recent years, deep learning has been extensively used in hyperspectral image (HSI) classification. The representative method is the convolutional neural network (CNN). However, due to the limitations of its inherent network backbone, CNNs still easily fail to mine some important information of HSIs, such as the sequence attributes of spectral signatures. To deal with this problem and make full use of the spectral-spatial information of HSIs, we propose a novel network named Multi-dimensional Information Expansion and Processing Network (MIEPN) for HSI classification, which is mainly composed of one information expansion module (IEM), one feature information expansion and extraction module (FEEM), and one ViT module. Briefly speaking, IEM expands and fuses HSI information in a three-dimensional (3D) space, yet FEPM pays more attention to digging deeper information. After these, the extracted information is input into the ViT module for HSI classification. Experiments carried out on several typical datasets demonstrate that the proposed network MIEPN can provide competitive results compared to the other state-of-the-art CNN-based methods.

In order to detect ships from the imagery of polarimetric synthetic aperture radar (PolSAR), a neighborhood polarimetric covariance matrix (for simplicity, we call it [$N$] hereinafter) was recently constructed. However, its calculation process is time-consuming and the backscattering heterogeneity near ship edges is also not well considered. For curing these shortcomings, we here propose two novel superpixel-based neighborhood polarimetric covariance matrices. In brief, the first matrix denoted by [SN] uses the simple linear iterative clustering (SLIC) to yield superpixels, whereas in the second matrix denoted by [GN], the gradient operator Sobel is adopted to obtain superpixels. Based on these two different kinds of superpixels, then, two different feature vectors$v_{\text {SN}}$and$v_{\text {GN}}$are separately built to compute [SN] and [GN]. Experiments performed on the real PolSAR datasets show that, compared to [$N$], [SN] and [GN] can improve the performance of the polarimetric whitening filter (PWF) more significantly and the time consumptions of calculating [SN] and [GN] are both much less.

Ship detection of polarimetric synthetic aperture radar (PolSAR) plays an important role in marine monitoring and ocean protection. Over the past years, local spatial information around pixels has been successfully applied to this task. However, few works have been done on PolSAR ship detection using the nonlocal spatial information (NSI). Within this context, we here propose one NSI-guided ship detection method PMR. Briefly speaking, the feature power difference (PD) is first constructed by computing the total power difference between the center pixelcand its most similar nonlocal pixeliwithin a 7×7 window. Then, the polarimetric feature reflection symmetry (RS) is introduced into PD to construct the method PMR (i.e., PD Multiply RS) for further enhancing the target-to-clutter ratio (TCR) and improving the ship detection accuracy. Experiments carried out on three real PolSAR datasets show that, in comparison with some other methods, especially the recently proposed local neighborhood information-based ship detector PWFN, PMR is more apt for ship detection. On average, its figure of merit (FoM) and TCR values respectively surpass PWFN0.24 and 12.83 dB.

Ship detection using polarimetric SAR (PolSAR) images has recently been an active topic in the Earth observation field. There, how to detect small ships is an open and challenging issue. Within this context, we put forward a two-stage ship detection model, by which a novel ship detection method is proposed as well. Briefly, in the first stage, a suppression manipulation is adopted to suppress sea clutter, where the feature SVVSOis built on the intensity information with the orientation angle compensation (OAC). In the second stage, an enhancement manipulation is further executed to highlight ships from the suppressed sea clutter, where the features PID (polarimetric intensity difference) and NsD (nonsurface degree) are first constructed with SVVSOand a series of theoretical derivations. Then, via fusing PID and NsD together, the two-stage-based method FPAN is proposed to detect ships. To demonstrate its performance, we apply FPAN to four different L-Band PolSAR datasets. Experimental results reveal that, compared to other state-of-the-art methods, especially the DBSPCPmethod, FPAN is more effective in detecting small ships. On average, its figure-of-merit (FoM) and target-to-clutter ratio (TCR) values are, respectively 9.40% and 25.18% greater than those of DBSPCP, while the time consumption is just 58.67% of the latter.

Ship detection of polarimetric SAR (PolSAR) imagery has attracted lots of attentions in recent years. Also, it is known that among the polarimetric channels$HH, HV$, and$VV, VV$is the most sensitive to sea clutter. Following this guidance, in this paper, a novel ship detector SVVS is first proposed via subtracting the term$C_{33}$from the total power detector SPAN. And then, the complect polarimetric covariance difference matrix [$CP$] is utilized to calculate SVVS, leading to the construction of another novel ship detector$\text{SVVS}_{CP}$. Finally, we investigate the statistical distribution of sea clutter with$\text{SVVS}_{CP}$and further develop an adaptive$\text{SVVS}_{CP}$-based C-FAR detector for ship detection. The experiment carried out on one real PolSAR imagery shows that, compared to SPAN, both SVVS and$\text{SVVS}_{CP}$hold better ship detection performances.

In this letter, we first investigate the relationship between polarimetric covariance matrix [C] and complete polarimetric covariance difference matrix [CP], and then construct a scattering difference parameter SDP. Subsequently, a hybrid polarimetric covariance matrix [HC] is developed based on SDP for curing the disadvantage of [CP], that is the scattering difference information of small ships cannot be well contained in [CP]. By fusing the feature “1-SDP” and the power detector SPANHCderived from [HC] together, a novel ship detection method SPANSDPis finally proposed to detect ships. Experiments performed on the airborne SAR (AIRSAR) L-Band and GF-3 C-Band data verify that 1) SPANSDPcan detect small ships more accurately than other state-of-the-art methods and 2) [HC] is more effective in improving ship detectors' detection performances in comparison with [CP].

In the smart retail industry, the quality of image collection is known to heavily affect the final analysis results (like accuracy) of applications, such as commodity detection, identification, and stitching. In practice, images captured manually by a monocular camera like mobile phone contain many low-quality images caused by an irregular shoot step. After image collection, filtering out low-quality images is a key step to mitigate the aforementioned impacts. One of the most effective solutions is to filter images with huge off-angles in 3-D through the shelf pose estimation algorithm. However, most of the existing camera pose estimation algorithms are designed for natural scenes and are difficult to realize in the structured real target scenes (like shelf scenes). Meanwhile, due to the lack of shelf pose dataset in academia and industry, there is still no approach designed for the shelf pose estimation in the smart retail scenario. In this article, we try to regress the complete shelf pose within a single end-to-end network and propose a novel geometry-supervised pose network (GSPN), which supervises the shelf pose estimation by learning the intrinsically geometric properties of shelves. Furthermore, we introduce the first retail shelf pose dataset (RSPD), including 28 876 images selected from three different shelf categories and being annotated carefully, as well as a complete 3-D shelf posture. The whole networks can be trained end to end with the shelf images and well-annotated ground truth. Experiments result of five strategies show that GSPN achieves the state-of-the-art performance on RSPD.

In this paper, a new scattering difference parameter named as SDP is first constructed to characterize the relationship between polarimetric covariance matrix [C] and complete polarimetric covariance difference matrix [CP]. Then, by integrating ”1-SDP” and the power maximization synthesis detector (PMS) derived from [CP], a novel ship detection method OmSPcpis further developed. In order to demonstrate the performance of the proposed method, one AIRSAR L-Band Polarimetric SAR dataset with 22 ships is exploited. The experimental results show that, compared to other methods, OmSPcpcan hold a better ship detection accuracy.

In this article, we investigate the scattering components of ships and find that the surface scattering may be the primary scattering for some ships, especially small ships. Meanwhile, the drawbacks of the complete polarimetric covariance difference matrix [CP] are also pointed out in theory. Based on these analyses, two new methods are then constructed to detect the ships. More specifically, the first one RsP is constructed by directly combining the similarity parameter of surface scattering Rs and the power-maximization synthesis (PMS) detector. The second one RsDVH is designed by taking advantage of four different features (i.e., Rs, double-bounce scattering, volume scattering, and helix scattering), which are all derived from the joint polarimetric information that is developed by combing the information of the polarimetric covariance matrix [C] and [CP]. Subsequently, the generalized Gamma distribution (GΓD) is found suitable for characterizing the RsDVH values of the sea clutter. At last, an adaptive constant false-alarm-rate (CFAR) detector developed from RsDVH is proposed for ship detection. To verify the effectiveness of RsP and RsDVH, four polarization synthetic aperture radar (PolSAR) imageries are tested, including one L-band UAVSAR imagery with 19 ships, two L-band AIRSAR imageries with 22 and 53 ships, respectively, and one C-band GF-3 imagery with ten ships. The experimental results show that: 1) the surface scattering is beneficial to detecting ships, especially the ships with prominent surface scatterings; 2) compared with other state-of-the-art methods, RsDVH can more effectively enhance the target-to-clutter ratio (TCR) values of small ships in the case of rough sea surface; and 3) the joint polarimetric information that is put forward and exploited for the first time in this article has a greater potential to help ship detectors improve their detection performances than the traditional polarimetric information included in [C].

Aircraft detection from high resolution remote sensing image is a challenging task due to the lack of annotation information, large-scale image size, and sparse distribution of aircraft. Recently, some convolutional neural network(CNN) based methods explore the attention based weakly supervised way to localize the aircraft without manual annotation information. However, the detection results are not satisfied with high false detection ratio. In this paper, a method of utilizing weakly supervised attention model to localize the multi-scale aircrafts is presented, in which the attention model is carried out in a weakly supervised way. Compared with other CNN based method, the proposed attention model can obtain more accurate attention map and localize the aircrafts more precisely. The experimental results on two challenging datasets demonstrate that the proposed method achieves higher detection accuracy and lower false detection ratio than other methods.

Sea surface and ship have different backscattering mechanisms, in which surface scattering is predominant for sea surface in the low sea state case. Based on this fact, many ship detectors have been developed by suppressing the surface scattering resulted from sea surface. Actually, small ship may also have strong surface scattering sometimes. In such a case, the methods of avoiding using surface scattering features may easily miss the detection of small ships. To verify this point, in this paper, we first analyze the shortcomings of An's method which is based on surface scattering similarity and the power maximization synthesis detector (PMS), and then improve it for detecting small ships more effectively. In order to demonstrate the performance of the proposed method, AIRSAR L-Band Polarimetric SAR dataset is exploited. Comparing to other methods, the new method shows a better ship detection performance.

BACKGROUND: Protein subcellular localization plays a crucial role in understanding cell function. Proteins need to be in the right place at the right time, and combine with the corresponding molecules to fulfill their functions. Furthermore, prediction of protein subcellular location not only should be a guiding role in drug design and development due to potential molecular targets but also be an essential role in genome annotation. Taking the current status of image-based protein subcellular localization as an example, there are three common drawbacks, i.e., obsolete datasets without updating label information, stereotypical feature descriptor on spatial domain or grey level, and single-function prediction algorithm's limited capacity of handling single-label database. RESULTS: In this paper, a novel human protein subcellular localization prediction model MIC_Locator is proposed. Firstly, the latest datasets are collected and collated as our benchmark dataset instead of obsolete data while training prediction model. Secondly, Fourier transformation, Riesz transformation, Log-Gabor filter and intensity coding strategy are employed to obtain frequency feature based on three components of monogenic signal with different frequency scales. Thirdly, a chained prediction model is proposed to handle multi-label instead of single-label datasets. The experiment results showed that the MIC_Locator can achieve 60.56% subset accuracy and outperform the existing majority of prediction models, and the frequency feature and intensity coding strategy can be conducive to improving the classification accuracy. CONCLUSIONS: Our results demonstrate that the frequency feature is more beneficial for improving the performance of model compared to features extracted from spatial domain, and the MIC_Locator proposed in this paper can speed up validation of protein annotation, knowledge of protein function and proteomics research.

In paper, we present a novel multi-part compact bilinear convolutional neural network (CNN) model, which consists of a bilinear CNN and two part-networks aiming to learn the global features and the finer local features simultaneously. The bilinear operation is simplified with recently proposed compact bilinear pooling method, and bilinear vectors are averagely pooled to keep more local spatial information. The proposed model is trained by using a histogram loss function in order to reduce the distribution overlap of positive pairs and negative pairs. Experiments show that, the combination of compact bilinear CNN and histogram loss can significantly improve the original models, and performs favorably compared to the state of the art.

Matching a specific person across non-overlapping cameras, known as person re-identification, is an important yet challenging task owing to the intra-class variations of the images from the same person in pose, illumination, and occlusion. Most existing body-parts based deep methods simply concatenate the features or scores obtained from spatial parts and ignore the complex spatial correlation between them. In this paper, we present a bi-directional Long Short-Term Memory (Bi-LSTM) architecture that can process the spatial parts sequentially, and enable the messages of different parts to go through in a bi-directional manner. Therefore, the spatial and contextual visual information can be modeled efficiently by the bi-directional connections and the internal gating function in LSTM. Furthermore, we propose a modified triplet loss to learn more discriminative features to distinguish positive pairs from negative pairs. Experiments on CUHK01 and CUHK03 datasets are carried out to demonstrate the effectiveness of the proposed method.

Ship target detection using PolSAR data has been an active research area and many algorithms have been developed in recent years. In this paper, we present a new method based on the difference between the ship pixels and background pixels, using a model similar to LBP (Local Binary Pattern). After that, the polarimetric signature method, namely, the SPAN (total power) detector, is used to detect ships. We adopt one Radarsat-2 data set with four-look processing for experiment, which was obtained in the Strait of Gibraltar ocean area. In comparing with other methods, we find that the result of our method is better than other detectors.