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Workshop on Hyperspectral image and Signal processing: Evolution in remote sensing, WHISPERS

作者： Corneliu Florea Andrei Racoviteanu Mihai Ivanovici Image Processing Analysis Laboratory National University of Science and Technology POLITEHNICA Bucharest Romania MIV Laboratory Transilvania University of Braşov Braşov Romania

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Automatic monitoring of agricultural crops with remote sensing data allows significant improvement in the face of climate change and environmental degradation. Hyperspectral imaging supports this process with spectrally detailed data, which goes beyond visible spectrum. In this paper we investigate how much information is actually brought by the richness and the details of additional spectral information. The assumed perspective is that of agricultural crop semantic segmentation approached with a powerful visual transformer based solution. Into more detail, we explore the effectiveness of various spectral bands in hyperspectral images, used as input, measured by five metrics quantifying the performance of agricultural crop segmentation. The evaluation relies on the UAV-HSI database. The primary conclusion is that there is significant redundancy in hyperspectrum, yet the full spectrum provides always top results. A secondary conclusion is that the recognition of specific crops such as “cotton” and “holly” benefits from removal of certain bands.

关键词： Measurement Visualization Semantic segmentation Face recognition Redundancy Crops Signal processing Transformers Monitoring Hyperspectral imaging

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Research on Full-Sky Star Identification Based on Spatial Projection and Reconfigurable Navigation Catalog

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remote sensing 2025年第9期17卷 1553-1553页

作者： Wu, Siyao Sun, Ting Xing, Fei Liu, Haonan Song, Jiahui Yu, Shijie Beijing Informat Sci & Technol Univ Sch Instrument Sci & Optoelect Engn Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Lab Intelligent Microsyst Beijing 100192 Peoples R China Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China

A star tracker is widely used as a high-precision attitude measurement device for spacecraft. It calculates attitude by extracting the magnitude and the position of presumed detected stars by a CCD/CMOS sensor and matching them with stars in the star catalog. The traditional star identification methods typically require the selection of specific anchor stars, which may cause insufficient identification accuracy as the number of stars used in the rough search is limited. In this paper, we propose a star identification method based on spatial projection, which starts with preprocessing. Then, a method for online expansion and reconstruction of the star catalog is proposed, which provides more stored star data. After the rough recognition and coordinate system transformation, the final identification is realized in the polar coordinate system. All the star points in the star image are identified, and the attitude information is obtained at the same time. The performance of the identification method is verified by real night sky experiments. Stray light experiments are also carried out to prove good noise immunity capabilities. Compared with the traditional subgraph isomorphism method, the proposed method makes it easier to adjust the number of recognizable stars in the field of view and better recognition of specific areas. The method is of great significance for future tasks such as attitude measurement, celestial navigation, remote sensing measurement, and space target observation and tracking.

关键词： star identification star image processing spatial projection reconfigurable navigation catalog celestial navigation

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Enhanced Multi-Scale Ship Detection Through Joint Multi-Source image Training

Enhanced Multi-Scale Ship Detection Through Joint Multi-Sour...

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International Conference on Information, Communication and Networks (ICICN)

作者： Yuan Huang Minghua Cao Yue Zhang Cheng Ning School of Computer and Communication Lanzhou University of Technology Lanzhou China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

In order to improve the detection performance of traditional single-source remote sensing image ship detection methods in terms of anti-interference capability and multi-scale targets. We develop an improved algorithm based on YOLOv8, called Sea-YOLOv8, and obtain the multi-source remote sensing image detection model SAR-YOLOv8 through migration ***, Efficient Multi-Scale Convolution (EMSConv) is used to improve the ability of the C2f module in capturing the features of ships at different scales by using different sizes of convolution kernels. scale ship features using different sized convolution kernels. Secondly, Large Separable Kernel Attentio (LSKAtt) is applied to the SPPF module to decompose the 2D convolution kernel into horizontal and vertical 1D kernels, which improves the computational efficiency while maintaining the high efficiency image processing capability. Experimental results on the SeaShips and SSDD datasets show that Sea-YOLOv8 has a good performance in terms of R, mAP, model parameters and FPS have achieved satisfactory results. Finally, the optimal transfer learning strategy was determined through layer-by-layer freezing, training and validation, which led to the construction of the multi-source remote sensing image recognition model SAR-YOLOv8.

关键词： Training image recognition Convolution Transfer learning Object detection Feature extraction Radar polarimetry Marine vehicles Kernel remote sensing

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Multi Class Spectral Discriminative Convolutional Neural Network Towards Hyperspectral image Classification of Crop Disease on Inclusion of Spectral Spatial Feature Fusion Mechanism 4

Multi Class Spectral Discriminative Convolutional Neural Net...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Boopathi Raja, A.S. Poovarasan, K. Udhayakiran, S. Muhammed Aleem, Shaik Tharageswari, K. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Crop diseases occurs due to bacterial, viral, parasitic and fungal infection leads to various challenges to farmer and agricultural based business as crop diseases effects the normal growth of the crop and it spreads to other crops surrounding it. Especially those crop infection leads to great economic damage to the farmers and to the entire country. In order to prevent, classify and mitigate those crop infections, effective measures have to be carried out. Manual observation by biologist and agriculturist leads to misinterpretation and time consuming. In particular, remote sensing technologies using hyperspectral imaging techniques helps to monitor and acquire the agriculture information in various target regions effectively. Due to development of computer vision technique and artificial intelligence approaches in form of machine learning and deep learning architectures, countermeasure against the crop disease can be achieved easily on processing the acquired information in form of hyperspectral images. However traditional architecture faces multiple challenges in form of the spectral signatures unmixing as boundaries of the objects of different diseases types is very complex to be filtered. Thus, a new spectral unmixing based object recognition method has to be constructed to categorizes different form of crop disease in the agriculture region. In this paper, Multi Class Spectral Discriminative Convolution Neural Network(MCSD-CNNN) using spectral and spatial feature fusion towards hyperspectral image classification for detect, classify and mitigate the crop diseases on various climate and various regions. Initially image preprocessing is carried out to eliminate the spectral noises. Preprocessed image is projected to convolution layer of the network to extract the endmembers of the spectral band representing the various crop species in the different climate and spatial regions. Extracted end member of the spectral band is represented spectral disease index map. Th

关键词： Convolutional neural networks

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Journey into gait biometrics: Integrating deep learning for enhanced pattern recognition

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DIGITAL SIGNAL processing 2024年 147卷

作者： Parashar, Anubha Parashar, Apoorva Rida, Imad Manipal Univ Jaipur Sch Comp & Informat Technol Jaipur Rajasthan India Mahindra Integrated Business Solut Emerging Technol Mumbai India Univ Technol Compiegne BMBI Lab F-60200 Compiegne France

Exploring Gait Biometrics within the domain of deep learning offers a potent fusion that significantly enhances pattern recognition capabilities. Over the past decade, the evolution of deep learning (DL) pipelines has showcased their effectiveness in overcoming complex challenges within image and signal processing applications. Constructing these pipelines requires a deep understanding of the diverse intermediate layers and their implications. The iterative refinement process involves careful selection and rigorous performance validation of each configuration, demanding significant time and contemplation. Consequently, the task of selecting a robust DL pipeline that excels across various datasets remains challenging. The central objective of this review is to provide guidance to researchers, fostering a comprehensive grasp of distinct gait sensing technologies, while establishing a solid foundation in deep learning concepts. Although gait recognition is a relatively recent development and is yet to find widespread application in real-world scenarios, this article offers a thorough examination of gait biometrics tailored specifically for real-time surveillance applications. Delving into the complexities, it elucidates the crucial parameters governing deep learning pipelines and their nuanced selection to address specific challenges. Through an analysis of recent research articles on deep learning models and their performance across diverse datasets, the review outlines the merits and demerits of various approaches. The ultimate aim is to facilitate the development of an optimized pipeline that seamlessly integrates existing methodologies, enabling the attainment of swift yet precise results for a given problem.

关键词： Gait recognition Biometrics Deep learning Surveillance pattern recognition

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Research on Intelligent Monitoring Technology of Substation Equipment Based on Artificial Intelligence

Research on Intelligent Monitoring Technology of Substation ...

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2023 IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2023

作者： Kexin, Zhang Siwei, Han Yang, Lin Jianquan, Liang Yue, Wang Chunfeng, Yue Lu, Li Dewen, Zhang Jian, Zhang State Grid Heilongjiang Electric Power Research Institute Harbin China

ISBN: (纸本)9798350313734

In this paper, based on fusion monitoring technology, communication technology, information processing technology, the establishment of substation remote intelligent inspection system. The system adopts browser/server (B/S) three-layer network distribution structure, which is divided into application end, network communication end and server end. Intelligent multi-dimensional image monitoring system realizes the remote real-time video monitoring function of substation, automatic inspection function of substation equipment, video intelligent analysis and remote alarm function. The system obtains substation surveillance video data through the unified video surveillance platform (SG-UVP). The intelligent identification and judgment of the running state of key equipment in substation by using video can realize the centralization, intellectualization and visualization of inspection. The practice proves that the system has significant economic and social benefits to improve the efficiency of substation operation and maintenance. © 2023 IEEE.

关键词： pattern recognition

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Morphological slum index for slum extraction from high-resolution remote sensing imagery over urban areas

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GEOCARTO INTERNATIONAL 2022年第26期37卷 13904-13922页

作者： Prabhu, R. Parvathavarthini, B. Anna Univ Dept Elect & Commun Engn St Josephs Coll Engn Chennai Tamil Nadu India Anna Univ Dept Comp Sci & Engn St Josephs Coll Engn Chennai Tamil Nadu India

This article proposes the Morphological Slum Index (MSI) approach for extracting slum areas from Very High Resolution (VHR) satellite images. An MSI is built by using different morphological operators. Since, the non-slum objects (buildings, vegetation, roads and open areas) are brighter than their neighbourhoods and show similar spectral properties as those of slums, MSI classifies the non-slum objects as slum objects. To optimize the misclassification of MSI, a post-processing technique called Morphological Spatial pattern Analysis (MSPA) is used. Three different VHR images acquired by the WorldView-2 sensor (1.84 m resolution) of Madurai city, India and one image acquired by the WorldView-3 Sensor (0.31 m resolution) of Kibera, Kenya are used as the test images to investigate the qualitative and quantitative results of the proposed technique. From the classified outputs, the proposed MSI with MSPA approach attains an overall accuracy of 95.78%, 96.42%, 95.12% and 92.64% for test images 1, 2, 3 and 4, respectively.

关键词： Morphological slum index slums morphological spatial pattern analysis WorldView-2 WorldView-3

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Self-supervised learning with randomised layers for remote sensing

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ELECTRONICS LETTERS 2021年第6期57卷 249-251页

作者： Jung, Heechul Jeon, Taegyun Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea SIA Daejeon South Korea

This letter presents a new self-supervised learning approach based on randomised layers for remote sensing. Our method is basically based on the Tile2Vec approach, which is one of the state-of-the-art self-supervised learning approaches for remote sensing. Unlike the original Tile2Vec algorithm, we reformulate the triplet loss as a classification loss. We use several fully connected layers with binary cross-entropy loss instead of no fully connected layers with triplet loss of the original Tile2Vec. We observe that not updating the fully connected layers is more helpful in obtaining more robust representations. The proposed algorithm is verified and evaluated by applying it to a cropland data layer classification task. The experimental results show that our approach is superior to the original Tile2Vec approach in all experiments based on random forest, logistic regression, and multi-layer classifiers.

关键词： Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research image recognition Programming and algorithm theory Computer vision and image processing techniques Combinatorial mathematics Regression analysis Regression analysis Supervised learning Other learning models (inc. Naive Bayes)

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Rotated Multi-Scale Interaction Network for Referring remote sensing image Segmentation

Rotated Multi-Scale Interaction Network for Referring Remote...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Sihan Liu Yiwei Ma Xiaoqing Zhang Haowei Wang Jiayi Ji Xiaoshuai Sun Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Referring remote sensing image Segmentation (RRSIS) is a new challenge that combines computer vision and natu-ral language processing. Traditional Referring image Seg-mentation (RIS) approaches have been impeded by the com-plex spatial scales and orientations found in aerial imagery, leading to suboptimal segmentation results. To address these challenges, we introduce the Rotated Multi-Scale In-teraction Network (RMSIN), an innovative approach de-signed for the unique demands of RRSIS. RMSIN incorpo-rates an Intra-scale Interaction Module (IIM) to effectively address the fine-grained detail required at multiple scales and a Cross-scale Interaction Module (CIM) for integrating these details coherently across the network. Furthermore, RMSIN employs an Adaptive Rotated Convolution (ARC) to account for the diverse orientations of objects, a novel contribution that significantly enhances segmentation accu-racy. To assess the efficacy of RMSIN, we have curated an expansive dataset comprising 17,402 image-caption-mask triplets, which is unparalleled in terms of scale and vari-ety. This dataset not only presents the model with a wide range of spatial and rotational scenarios but also estab-lishes a stringent benchmark for the RRSIS task, ensuring a rigorous evaluation of performance. Experimental eval-uations demonstrate the exceptional performance of RM-SIN, surpassing existing state-of-the-art models by a signif-icant margin. Datasets and code are available at https://***/Lsan2401/RMSIN.

关键词： image segmentation Computer vision Codes Navigation Convolution Benchmark testing Solids

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SpaceNet 8-The Detection of Flooded Roads and Buildings

SpaceNet 8-The Detection of Flooded Roads and Buildings

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Haensch, Ronny Arndt, Jacob Lunga, Dalton Gibb, Matthew Pedelose, Tyler Boedihardjo, Arnold Petrie, Desiree Bacastow, Todd M. German Aerosp Ctr DLR Cologne Germany Oak Ridge Natl Lab Oak Ridge TN USA Maxar Technol Westminster CO USA

ISBN: (纸本)9781665487399

The frequency and intensity of natural disasters (i.e. wildfires, storms, floods) has increased over recent decades. Extreme weather can often be linked to climate change, and human population expansion and urbanization have led to a growing risk. In particular floods due to large amounts of rainfall are of rising severity and are causing loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Expanding urbanization along rivers and creeks often includes opening flood plains for building construction and river straightening and dredging speeding up the flow of water. In a flood event, rapid response is essential which requires knowledge which buildings are susceptible to flooding and which roads are still accessible. To this aim, SpaceNet 8 is the first remote sensing machine learning training dataset combining building footprint detection, road network extraction, and flood detection covering 850km2, including 32k buildings and 1,300km roads of which 13% and 15% are flooded, respectively.

关键词： Training image segmentation Satellites Roads Buildings Optical imaging Rivers

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