No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-scale Interaction

No-service rail surface defect (NRSD) segmentation is an essential way for perceiving the quality of no-service rails. However, due to the complex and diverse outlines and low-contrast textures of no-service rails, existing natural image segmentation methods cannot achieve promising performance in NRSD images, especially in some unique and challenging NRSD scenes. To this end, in this paper, we propose a novel segmentation network for NRSDs based on Normalized Attention and Dual-scale Interaction, named NaDiNet. Specifically, NaDiNet follows the enhancement-interaction paradigm. The Normalized Channel-wise Self-Attention Module (NAM) and the Dual-scale Interaction Block (DIB) are two key components of NaDiNet. NAM is a specific extension of the channel-wise self-attention mechanism (CAM) to enhance features extracted from low-contrast NRSD images. The softmax layer in CAM will produce very small correlation coefficients which are not conducive to low-contrast feature enhancement. Instead, in NAM, we directly calculate the normalized correlation coefficient between channels to enlarge the feature differentiation. DIB is specifically designed for the feature interaction of the enhanced features. It has two interaction branches with dual scales, one for fine-grained clues and the other for coarse-grained clues. With both branches working together, DIB can perceive defect regions of different granularities. With these modules working together, our NaDiNet can generate accurate segmentation map. Extensive experiments on the public NRSD-MN dataset with man-made and natural NRSDs demonstrate that our proposed NaDiNet with various backbones (i.e., VGG, ResNet, and DenseNet) consistently outperforms 10 state-of-the-art methods. The code and results of our method are available at https://github.com/monxxcn/NaDiNet.Comment: 10 pages, 6 figures, Accepted by IEEE Transactions on Instrumentation and Measurement 202

Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment

Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote sensing images (ORSI-SOD) are proposed. However, most methods ignore the huge parameters and computational cost brought by CNNs, and only a few pay attention to the portability and mobility. To facilitate practical applications, in this paper, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through L2 loss and used for detail enhancement. Finally, starting from the highest-level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76M parameters and running with 1.7G FLOPs for 288x288 inputs. Our code and results are available at https://github.com/MathLee/SeaNet.Comment: 11 pages, 4 figures, Accepted by IEEE Transactions on Geoscience and Remote Sensing 202

Classification of the average capillary pressure function and its application in calculating fluid saturation

When reservoir heterogeneity is strong, there is a great error between the calculated oil saturation based on the J-function and the actual oil saturation interpreted by logging. Aimed at this problem, a reservoir quality index is proposed to classify the average mercury curves, and the reservoir quality index model and original oil saturation model are established, by experimental measurement and numerical simulation with an illustration of an oilfield in the Pearl River Mouth Basin. The oil saturation calculated with this method accords closely with that interpreted by logging, it is a reliable method to show the properties of strongly heterogeneous reservoirs. In addition, this paper proposes a comprehensive utilization of mercury curve and mercury-ejection curve fitting J-function in establishing the saturation model of bound water, movable water, residual oil, movable oil. Considering the influences of such factors as reservoir quality index and clay content on mercury-ejection efficiency, “mercury-ejection index” is used to classify the average mercury-ejection curves and a movable oil saturation model is established, which provides basis for the calculation of recoverable reserves and the research of residual oil distribution. Key words: J-function, saturation model, mercury curve, mercury-ejection curve, reservoir quality index, mercury-ejection inde

Multi-content complementation network for salient object detection in optical remote sensing images

In the computer vision community, great progresses have been achieved in salient object detection from natural scene images (NSI-SOD); by contrast, salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. The unique characteristics of optical RSIs, such as scales, illuminations and imaging orientations, bring significant differences between NSI-SOD and RSI-SOD. In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. Specifically, MCCNet is based on the general encoder-decoder architecture, and contains a novel key component named Multi-Content Complementation Module (MCCM), which bridges the encoder and the decoder. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features, and exploit the content complementarity between them to highlight salient regions over various scales in RSI features through the attention mechanism. Besides, we comprehensively introduce pixel-level, map-level and metric-aware losses in the training phase. Extensive experiments on two popular datasets demonstrate that the proposed MCCNet outperforms 23 state-of-the-art methods, including both NSI-SOD and RSI-SOD methods. The code and results of our method are available at https://github.com/MathLee/MCCNet.Ministry of Education (MOE)This work was supported in part by the National Natural Science Foundation of China under Grant 62171269, in part by the China Scholarship Council under Grant 202006890079, and in part by the Singapore Ministry of Education Tier-2 Fund MOE2016-T2-2-057(S)

Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation

Salient object detection in optical remote sensing images (ORSI-SOD) has been widely explored for understanding ORSIs. However, previous methods focus mainly on improving the detection accuracy while neglecting the cost in memory and computation, which may hinder their real-world applications. In this paper, we propose a novel lightweight ORSI-SOD solution, named CorrNet, to address these issues. In CorrNet, we first lighten the backbone (VGG-16) and build a lightweight subnet for feature extraction. Then, following the coarse-to-fine strategy, we generate an initial coarse saliency map from high-level semantic features in a Correlation Module (CorrM). The coarse saliency map serves as the location guidance for low-level features. In CorrM, we mine the object location information between high-level semantic features through the cross-layer correlation operation. Finally, based on low-level detailed features, we refine the coarse saliency map in the refinement subnet equipped with Dense Lightweight Refinement Blocks, and produce the final fine saliency map. By reducing the parameters and computations of each component, CorrNet ends up having only 4.09M parameters and running with 21.09G FLOPs. Experimental results on two public datasets demonstrate that our lightweight CorrNet achieves competitive or even better performance compared with 26 state-of-the-art methods (including 16 large CNN-based methods and 2 lightweight methods), and meanwhile enjoys the clear memory and run time efficiency. The code and results of our method are available at https://github.com/MathLee/CorrNet.Comment: 11 pages, 6 figures, Accepted by IEEE Transactions on Geoscience and Remote Sensing 202

Improving the accuracy of geological model by using seismic forward and inverse techniques

Abstract: Sequential Gaussian simulation method is taken as an example to analyze the characteristics and defects of stochastic simulation methods, and a modeling strategy is proposed that is based on the results of seismic inversion to improve modeling accuracy. Stochastic simulation can only achieve “mathematical reality” by recovery of parameter's macro statistical regularity through examples, and seismic forward model can verify whether the simulation results deviate from the “geological reality”. Seismic forward modeling can verify the reliability of the geological model, and the results of geological modeling constrained by seismic inversion can improve the accuracy of the model to achieve the “geological reality”. For braided river delta, a modeling strategy constrained by seismic inversion results in the condition of “multi-levels” and “multi-conditions” is put forward. For lithofacies modeling, division of lithofacies in single well acts as the first variable; in the stage of exploration and development, lithology probability and lithology body act as the second variable respectively, and then establish facies models. For property modeling, take lithofacies model and the seismic impedance as the first variable and the second variable respectively to create property model, in the condition of horizontal and vertical impedance constraints. The proposed modeling strategy maintains the statistical regularities of input data, keep a better consistency with seismic data, and match with dynamic field production. Key words: stochastic simulation, variogram, seismic forward, seismic inversion, facies-controlled modeling, seismic constraint, constraint modelin