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UAV remote sensing imaging technology applied to agricultural plant protection

UAV remote sensing imaging technology applied to agricultura...

引用

2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Liu, Ying Nanjing Vocational Institute of Transport Technology China

ISBN: (纸本)9781510666351

This paper addressed the contradiction between the decrease of labor force and the increase of agricultural output demand in agricultural development, combined with the technical advantages of UAV imaging radar in SAR imaging, ground moving target detection and height measurement, analyzed the key technology of UAV imaging radar applied to agricultural plant protection. The signal processing scheme and processing process of UAV imaging radar for agricultural plant protection were designed, and the experimental verification results using the measured data were reasonable and feasible. The verification results of the measured data showed that the UAV remote sensing imaging technology can realize the high-resolution imaging and preliminary ground object classification in the operation area. © 2023 SPIE.

关键词： Radar imaging

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Comparative Analysis of Pre-trained Architectures for remote sensing image Scene Classification 1

引用

International conference on Advances in signal processing and Communication Engineering, ICASPACE 2023

作者： Atchaya, A. Josephine Priya, Asha Gnana Pranesh, M. Anitha, J. Department of ECE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9789819705627

ISBN: (纸本)9789819705610

remote sensing offers a comprehensive perspective and a practical overview of a region. Through remote sensing, we can monitor the physical characteristics of an area by measuring the radiation reflected and emitted remotely. In most cases, this is done by satellite or aircraft (It can be a drone image). It is possible to interpret a lot of information using remote sensing (RS). There is still a challenge in selecting and combining appropriate features from remote sensing imagery based on their spectral and spatial characteristics. A deep artificial neural network is a computing system that has the capacity to learn from data on its own using algorithms. In scene classification approaches, convolutional neural networks have been successfully applied to create a system capable of handling classification without human intervention. The pre-trained classification technique is suggested in this work to categorize the satellite images. The classifiers are implemented for pre-trained deep learning networks including AlexNet, Resnet50, GoogleNet, Vgg16, and Inception V3. The performance is measured in terms of classification accuracy (AC), sensitivity (SE), specificity (SP), and positive predictive value (PPV). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： remote sensing

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A Theoretical Framework for Unsupervised Land Cover Change Detection in Dense Satellite image Time Series 29

A Theoretical Framework for Unsupervised Land Cover Change D...

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conference on image and signal processing for remote sensing XXix

作者： Listiani, Indira Aprilia Bovolo, Francesca Zanetti, Massimo Fdn Bruno Kessler Digital Soc Via Sommarive 18 I-38123 Trento Italy Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

ISBN: (纸本)9781510666955;9781510666962

This paper addresses the complex task of detecting and characterizing changes in dense Satellite image Time Series (SITS). Although Change Vector Analysis (CVA) is widely used for Change Detection (CD), it has limitations due to missing prior information on changes, such as: optimal spectral channels and change timing. Time series data can help overcome these limitations, but working with them is challenging. To address these challenges, the paper introduces a novel framework called Time Series Change Vector Analysis (TSCVA), which builds upon the principles of CVA. In TSCVA, the paper redefines CVA in the time series feature space and introduces new definitions for change in time series magnitude and direction. This allows for a detailed analysis of change components in the time and spectrum domain within the SITS, enabling unsupervised CD. We utilize the expectation-maximization algorithm to estimate parameters of statistical distributions for change and nochange classes. The effectiveness of the proposed TSCVA method is evaluated using Sentinel-2 time series data. The results, both quantitative and qualitative, confirm the robustness of this approach in effectively addressing the CD problem in dense SITS.

关键词： remote sensing Multi-Temporal Multi-spectral Unsupervised Change Detection (CD) Satellite image Time Series (SITS) Change Vector Analysis (CVA)

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MDFNet: Multimodal Feature Decomposition and Fusion Network for Multimodal remote sensing image Semantic Segmentation 2

MDFNet: Multimodal Feature Decomposition and Fusion Network ...

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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Wei, Tianyu Chen, He Wang, Jue Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

The semantic segmentation of multimodal remote sensing (RS) images, utilizing optical and synthetic aperture radar (SAR) data, has raised attention in recent studies. Advanced studies adaptively modeled and fused modality-share and modality-specific information beneficial for segmentation and achieved competitive performance. However, in challenging scenarios such as low image contrast or blurred textures in optical images and speckle noises or foreshortening in SAR images, modality-specific information may lost. This study proposed a multimodal feature decomposition and fusion network (MDFNet) designed for multimodal RS image semantic segmentation using optical and SAR images. By decomposing multimodal features into modality-share and modality-difference features and applying gradient descent on modality-difference features, modality-specific information beneficial for segmentation can be retained. Specifically, we designed a MDF decoder with MDF blocks. MDF block maps multimodal features into the same feature space and calculates the difference of multimodal features to obtain modality-share and modality-difference features respectively, then fuses these features. MDF decoder optimizes modality-difference features by gradient descent using adaptive modeling, thereby retaining modality-specific information that is beneficial for segmentation. Comprehensive experiments conducted on the DFC20 dataset demonstrated that the proposed MDFNet surpasses representative methods. © 2024 IEEE.

关键词： Semantic Segmentation

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EVALUATION OF A MARINE MESOSCALE EVENTS CLASSIFIER

EVALUATION OF A MARINE MESOSCALE EVENTS CLASSIFIER

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IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Reggiannini, Marco Papini, Oscar Pieri, Gabriele Natl Res Council Italy Inst Informat Sci & Technol Via G Moruzzi 1 I-56124 Pisa Italy

ISBN: (纸本)9798350302615

Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.

关键词： Mesoscale events Sea surface temperature image processing Environmental monitoring Statistical classification Upwelling classification

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Research on remote sensing Satellite image Compression and Evaluation Methods Based on GAN

Research on Remote Sensing Satellite Image Compression and E...

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IET International Radar conference 2023, IRC 2023

作者： Cheng, Kan Zou, Yafang Lu, Zheng Jin, Hao Zhao, Yuting Xing, Kunpeng China Academy of Space Technology Beijing China

ISBN: (纸本)9781839539954

image compression technology plays an indispensable role in the field of remote sensing satellite;however, existing methods often struggle to avoid loss of fine details in remote sensing satellite images. This paper introduces a remote sensing satellite image compression model based on Generative Adversarial Networks (GANs). The core objective of this model is to effectively preserve image details during the compression process and generate high-quality reconstructed images. Additionally, we incorporate object detection as part of the evaluation method for the compression model to comprehensively explore the feasibility and effectiveness of different compression methods in practical applications. Extensive experimental results on a real remote sensing satellite image dataset demonstrate that our model can maintain high-quality image reconstruction while reducing data volume, particularly excelling in object detection performance. © The Institution of Engineering & Technology 2023.

关键词： Generative adversarial networks

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Onboard deep learning for efficient small satellite reflectance retrievals and downlink 29

Onboard deep learning for efficient small satellite reflecta...

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conference on image and signal processing for remote sensing XXix

作者： Garimella, Sarvesh MyRadar 111 W Jefferson St Orlando FL 32801 USA

ISBN: (纸本)9781510666955;9781510666962

The rapid pace of commercial space launches has drastically increased opportunities for novel small satellite missions. Though small satellites benefit from reduced launch costs, they are constrained by their Size, Weight, and Power (SWaP) limitations. The satellites in MyRadar's HORIS (Hyperspectral Orbital remote Imaging Spectrometer) constellation have SWaP constraints of a 1U CubeSat form factor (10 cm3 and < 1.33 kg), which place significant limits on mission design and duty cycle. In particular, downlinking large amounts of raw data, like that generated by the HORIS narrow band hyperspectral sensor, can be prohibitively bandwidth and power intensive. This study explores using deep learning inference to optimize onboard data processing and to mitigate the impacts of data volume on downlinking hyperspectral information from LEO (Low Earth Orbit). In particular, deep learning inference on the visible wavelength context imagery is used to constrain the aerosol model effects on reflectance retrieval calculations to convert at-sensor radiance measurements into surface (or cloud top) reflectance estimates. Also, a transfer learning approach utilizing an adversarially trained autoencoder to compress data from other satellites is used for HORIS data compression to reduce the required power and bandwidth for alerting use cases.

关键词： remote sensing satellite CubeSat artificial intelligence alerting wildfires compression radiative transfer

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FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional Pattern for Perceptual image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional Patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 IEEE.

关键词： Hamming distance

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Drone-Based Multispectral Imaging and Deep Learning for Timely Detection of Branched Broomrape in Tomato Farms 9

Drone-Based Multispectral Imaging and Deep Learning for Time...

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conference on Autonomous Air and Ground sensing Systems for Agricultural Optimization and Phenotyping ix

作者： Narimani, Mohammadreza Pourreza, Alireza Moghimi, Ali Mesgaran, Mohsen Farajpoor, Parastoo Jafarbiglu, Hamid Univ Calif Davis Dept Biol & Agr Engn Davis CA 95616 USA Univ Calif Davis Dept Plant Sci Davis CA 95616 USA

ISBN: (纸本)9781510674257;9781510674240

This study addresses the escalating threat of branched broomrape (Phelipanche ramosa) to California's tomato industry, which provides over 90% of the United States processing tomatoes. The parasite's life cycle, largely underground and therefore invisible until advanced infestation, presents a significant challenge to both detection and management. Conventional chemical control strategies, while widespread, are costly, environmentally detrimental, and often ineffective due to the parasite's subterranean nature and the indiscriminate nature of the treatments. Innovative strategies employing advanced remote sensing technologies were explored, integrating drone-based multispectral imagery with cutting-edge Long Short-Term Memory (LSTM) deep learning networks and utilizing Synthetic Minority Over-sampling Technique (SMOTE) to address the imbalance between healthy and diseased plant samples in the data. The research was conducted on a known broomrape-infested tomato farm in Woodland, Yolo County, California. Data were meticulously gathered across five key growth stages determined by growing degree days (GDD), with multispectral images processed to isolate tomato canopy reflectance. Our findings revealed that the earliest growth stage at which broomrape could be detected with acceptable Accuracy was at 897 GDD, achieving an overall Accuracy of 79.09% and a recall rate for broomrape of 70.36%, without the integration of subsequent growing stages. However, when considering sequential growing stages, the LSTM models applied across four distinct scenarios with and without SMOTE augmentation indicated significant improvements in the identification of broomrape-infested plants. The best-performing scenario, which integrated all growth stages, achieved an overall Accuracy of 88.37% and a Recall rate of 95.37%. These results demonstrate the LSTM network's robust potential for early broomrape detection and highlight the need for further data collection to enhance the model's pract

关键词： Branched broomrape remote sensing multispectral imagery image processing deep learning Long Short-Term Memory (LSTM) Synthetic Minority Over-sampling Technique (SMOTE) time series analysis

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Joint Semantic Segmentation of Optical and SAR image in Hazy Environments via Cross-modal Information Rectification and Cross-attention Fusion

Joint Semantic Segmentation of Optical and SAR Image in Hazy...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Fan, Xinyue Zhang, Libao School of Artificial Intelligence Beijing Normal University Beijing100875 China

ISBN: (纸本)9798350368741

Semantic segmentation is crucial in remote sensing image processing. In recent years, semantic segmentation using optical and SAR images for multi-modal fusion is gaining attention for its good results. The current research primarily encompasses two problems: 1) Existing fusion methods are designed for clear images and struggle in harsh weather. 2) Current fusion methods insufficiently capture multi-modal information correlation. This paper presents a joint semantic segmentation of optical and SAR in hazy environments network that incorporates channel fusion for feature enhancement and cross-attention for feature fusion, enabling efficient segmentation of hazy optical images. First, we design a channel-guided crossmodal information correction module. This module regards fog as noise and uses high-confidence features of one modality to calibrate the other modality, thereby reducing the impact of fog. Secondly, to address the issue of insufficient fusion of multimodal information, we introduce a cross-attention fusion module to effectively combine the complementary information from two streams leveraging the large receptive field enabled by self-attention. The implementation results show that this method has better results for the semantic segmentation of blurry remote sensing images. © 2025 IEEE.

关键词： multi-modal fusion optical images remote sensing SAR images Semantic segmentation

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