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Multi-sensor multispectral reconstruction framework based on projection and reconstruction

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Science China(Information Sciences) 2024年第3期67卷 246-262页

作者： Tianshuai LI Tianzhu LIU Xian LI yanfeng gu Yukun WANG Yushi CHEN School of Electronics and Information Engineering Harbin Institute of Technology Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing

The scarcity and low spatial resolution of hyperspectral images(HSIs) have become a major problem limiting the application of the images. In recent years, spectral reconstruction(SR) has been applied to convert multispectral images(MSIs) with abundant quantities and high spatial resolution into HSIs. With the launch of several new multispectral(MS) satellites with a short repeat period, the simultaneous acquisition of images from multiple MS sensors in the same area is gradually becoming feasible. Unfortunately, existing SR methods only consider the reconstruction of the MSIs of a single sensor without considering using MSIs from different MS sensors to obtain a better construction effect through their complementary bands. However,multi-sensor SR is characterized by two problems: inconsistency in the amplitude information of real multisensor imaging and difficulty in the extraction of the complex correlations of bands from different *** solve these problems, this paper proposes a multi-sensor SR framework based on a two-step approach in which the problems of amplitude inconsistency and band information extraction are solved using an ideal projection network and an ideal multi-sensor SR network, respectively. The effectiveness of the proposed method is verified by experiments on three datasets.

关键词： spectral reconstruction spectral superresolution hyperspectral image multispectral image neural networks multi-sensor

Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao gu yanfeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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清洁生产措施在石油井下作业中的应用

石化技术 2024年第5期31卷 278-280页

作者：谷焰锋中海油田服务股份有限公司一体化和新能源事业部天津300459

采用了系统分析的方法,通过采取不断改进工程设计、使用清洁原材料、先进工艺技术和机械设备、优化科学管理以及综合资源利用等多方面措施,实施了井下作业的洁净化生产,以实现企业可持续绿色发展。

关键词：石油井下作业清洁生产废物处理绿色技术标准体系

Parameter feature extraction for hyperspectral detection of the shallow underwater target

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Science China(Technological Sciences) 2021年第5期64卷 1092-1100页

作者： XIA Zhi gu yanfeng Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

For the detection of shallow underwater targets by the hyperspectrum, the information of the target is carried in the water-leaving radiation. However, the differences between the water-leaving radiation of the underwater target and the ocean background radiation are not significant, and the spectrum of the water-leaving radiation is sensitive to the changes of the marine environment. Therefore, taking the spectrum as the feature is not stable for hyperspectral detection of the underwater target. In this paper, we propose a parameter feature extraction method for hyperspectral detection of the shallow underwater target. The presence of an underwater target would change the optical properties of seawater in a local place. Based on that, a quasianalytical model of the water-leaving radiation is adopted to inverse the properties of the marine environment from the radiated spectrum. Then the inversed water depth and bottom reference reflectance are taken as the features. Simulation results show that the samples which belong to the water-leaving radiation of underwater target have higher inversed depth and reference reflectance than that belong to the ocean background radiation. Furthermore, these two kinds of samples can be separated significantly and stably in a 2-dimension feature space.

关键词： hyperspectral imaging underwater technology feature extraction

Class-guided coupled dictionary learning for multispectral-hyperspectral remote sensing image collaborative classification

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Science China(Technological Sciences) 2022年第4期65卷 744-758页

作者： LIU TianZhu gu yanfeng JIA XiuPing School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing Harbin 150001China School of Engineering and Information Technology University of New South WalesCanberra 2610Australia

The fine classification of large-scale scenes is becoming more and more important in optical remote sensing *** two kinds of typical optical remote sensing data,multispectral images(MSIs)and hyperspectral images(HSIs)have complementary *** MSI has a large swath and short revisit period,but the number of bands is limited with low spectral resolution,leading to weak separability of between class *** with MSI,HSI has hundreds of bands and each of them is narrow in bandwidth,which enable it to have the ability of fine classification,but too long in aspects of revisit *** make efficient use of their combined advantages,multispectral-hyperspectral remote sensing image collaborative classification has become one of hot topics in remote *** deal with the collaborative classification,most of current methods are unsupervised and only consider the HSI reconstruction as the *** this paper,a class-guided coupled dictionary learning method is proposed,which is obviously distinguished from the current ***,the proposed method utilizes the labels of training samples to construct discriminative sparse representation coefficient error and classification error as regularization terms,so as to enforce the learned coupled dictionaries to be both representational and *** learned coupled dictionaries facilitate pixels from the same category have similar sparse represent coefficients,while pixels from different categories have different sparse represent *** experiments on three pairs of HSI and MSI have shown better classification performance.

关键词： multimodal remote sensing multispectral image hyperspectral image collaborative classification class-guided coupled dictionary learning

A multi-frame sparse self-learning PWC-Net for motion estimation in satellite video scenes

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Science China(Information Sciences) 2023年第9期66卷 213-225页

作者： Tengfei WANG yanfeng gu Shengyang LI School of Electronics and Information Engineering Harbin Institute of Technology Technology and Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences

Motion estimation is an important approach to acquiring motion information of all targets in satellite video while it provides the ability to real-time monitor the Earth observation region. Compared with the case in computer vision, motion estimation in satellite video has to face two main difficulties: the large scale of observation and numerous weak targets of low signal-to-noise ratio. In this paper, a multi-frame sparse self-learning PWC-Net(MSSPWC-Net) is proposed to implement motion estimation of the weak targets in satellite video. To overcome the shortage that the existing PWC-Net fails to extract motion information from numerous weak targets, motion consistency and sparse self-learning are introduced to modify the pyramid,warping, and cost volume convolutional neural networks(CNN) network(PWC-Net). The motion consistency between neighboring frames as a multi-frame framework is mainly used to improve the accuracy of motion estimation of the weak targets, and sparse self-learning is adopted to deal with the case that labeled samples in satellite video are insufficient to train PWC-Net. Numerical experiments are conducted on 4 real satellite video datasets. Experimental results demonstrate that the proposed MSSPWC-Net achieves the excellent performance of motion estimation of the weak targets in satellite video and outperforms the state-of-the-art methods.

关键词： satellite video scenes motion estimation small blurry targets

Multi-attribute smooth graph convolutional network for multispectral points classification

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Science China(Technological Sciences) 2021年第11期64卷 2509-2522页

作者： WANG QingWang gu yanfeng YANG Min WANG Chen School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China Huawei Technology Co. Ltd.Shanghai 200120China North China Sea Marine Technical Support Center Ministry of Natural ResourcesQingdao 266000China

Multispectral points, as a new data source containing both spectrum and spatial geometry, opens the door to three-dimensional(3D) land cover classification at a finer scale. In this paper, we model the multispectral points as a graph and propose a multiattribute smooth graph convolutional network(Ma SGCN) for multispectral points classification. We construct the spatial graph,spectral graph, and geometric-spectral graph respectively to mine patterns in spectral, spatial, and geometric-spectral ***, the multispectral points graph is generated by combining the spatial, spectral, and geometric-spectral graphs. Moreover,dimensionality features and spectrums are introduced to screen the appropriate connection points for constructing the spatial graph. For remote sensing scene classification tasks, it is usually desirable to make the classification map relatively smooth and avoid salt and pepper noise. A heat operator is then introduced to enhance the low-frequency filters and enforce the smoothness in the graph signal. Considering that different land covers have different scale characteristics, we use multiple scales instead of the single scale when leveraging heat operator on graph convolution. The experimental results on two real multispectral points data sets demonstrate the superiority of the proposed Ma SGCN to several state-of-the-art methods.

关键词： multispectral points multi-attribute graph construction smooth graph convolution graph convolutional network(GCN) 3D land cover classification

A coupled multi-task feature boosting method for remote sensing scene classification

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Science China(Technological Sciences) 2023年第3期66卷 663-673页

作者： WANG TengFei gu yanfeng GAO guoMing ZENG XiaoPeng School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The scene classification plays an essential role in processing very high resolution(VHR)images for *** scene classification in remote sensing faces two difficulties:the mismatching features caused by the model overfitting problem and the semantic information losing *** multi-task method helps solve the problems by using the share weights of multiply *** propose a feature boosting method with a multi-task framework that combines the scene classification task and the semantic segmentation task to overcome the *** from the traditional multi-task learning method,the two tasks are coupled together via a weakly supervised learning method so that it does not require the labelled semantic segmentation ***,we proposed a weakly supervised segmentation method to create the interconnection of the segmentation task and the classification *** we achieve a coarse segmentation result which is highly correlated to the classification by the weakly supervised ***,according to the surface distribution of remote sensing,we propose a sparse surface constraint to obtain fine segmentation *** features are obtained by constraining the shared weights of the weakly supervised segmentation ***,we classify the scenes using the fine features and conduct experiments on the public remote sensing scene classification *** results demonstrate that the proposed coupled multi-task model outperforms the stateof-the-art methods on remote sensing scene classification.

关键词： scene classification coupled multi-task feature boosting

Deep feature extraction and motion representation for satellite video scene classification

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Science China(Information Sciences) 2020年第4期63卷 97-111页

作者： yanfeng gu Huan LIU Tengfei WANG Shengyang LI guoming GAO School of Electronics and Information Engineering Harbin Institute of Technology Technology and Engineering Center for Space Utilization Chinese Academy of Sciences The Key Laboratory of Space Utilization Chinese Academy of Sciences

Satellite video scene classification(SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite *** frames are firstly selected based on the blur detection and spatial activity of satellite *** the fine-tuned visual geometry group network(VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite *** motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis(PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory(LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC.A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC.

关键词： satellite videos classification convolutional neural network CNN long and short term memory LSTM motion representation