- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Generative Discriminatory Classified Network for Change Detection in Multispectral Imagery
摘要： Multispectral image change detection based on deep learning generally needs a large amount of training data. However, it is difficult and expensive to mark a large amount of labeled data. To deal with this problem, we propose a generative discriminatory classified network (GDCN) for multispectral image change detection, in which labeled data, unlabeled data, and new fake data generated by generative adversarial networks are used. The GDCN consists of a discriminatory classified network (DCN) and a generator. The DCN divides the input data into changed class, unchanged class, and extra class, i.e., fake class. The generator recovers the real data from input noises to provide additional training samples so as to boost the performance of the DCN. Finally, the bitemporal multispectral images are input to the DCN to get the final change map. Experimental results on the real multispectral imagery datasets demonstrate that the proposed GDCN trained by unlabeled data and a small amount of labeled data can achieve competitive performance compared with existing methods.
关键词： Change detection,deep learning,multispectral imagery,generative adversarial networks (GANs)
更新于2025-09-23 15:23:52
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A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle
摘要： Unmanned aerial vehicle (UAV)-based remote sensing (RS) possesses the significant advantage of being able to efficiently collect images for precision agricultural applications. Although numerous methods have been proposed to monitor crop nitrogen (N) status in recent decades, just how to utilize an appropriate modeling algorithm to estimate crop leaf N content (LNC) remains poorly understood, especially based on UAV multispectral imagery. A comparative assessment of different modeling algorithms (i.e., simple and non-parametric modeling algorithms alongside the physical model retrieval method) for winter wheat LNC estimation is presented in this study. Experiments were conducted over two consecutive years and involved different winter wheat varieties, N rates, and planting densities. A five-band multispectral camera (i.e., 490 nm, 550 nm, 671 nm, 700 nm, and 800 nm) was mounted on a UAV to acquire canopy images across five critical growth stages. The results of this study showed that the best-performing vegetation index (VI) was the modified renormalized difference VI (RDVI), which had a determination coefficient (R2) of 0.73 and a root mean square error (RMSE) of 0.38. This method was also characterized by a high processing speed (0.03 s) for model calibration and validation. Among the 13 non-parametric modeling algorithms evaluated here, the random forest (RF) approach performed best, characterized by R2 and RMSE values of 0.79 and 0.33, respectively. This method also had the advantage of full optical spectrum utilization and enabled flexible, non-linear fitting with a fast processing speed (2.3 s). Compared to the other two methods assessed here, the use of a look up table (LUT)-based radiative transfer model (RTM) remained challenging with regard to LNC estimation because of low prediction accuracy (i.e., an R2 value of 0.62 and an RMSE value of 0.46) and slow processing speed. The RF approach is a fast and accurate technique for N estimation based on UAV multispectral imagery.
关键词： UAV,multispectral imagery,radiative transfer model,LNC,vegetation index,non-parametric regression
更新于2025-09-23 15:22:29
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Online Mutual Foreground Segmentation for Multispectral Stereo Videos
摘要： The segmentation of video sequences into foreground and background regions is a low-level process commonly used in video content analysis and smart surveillance applications. Using a multispectral camera setup can improve this process by providing more diverse data to help identify objects despite adverse imaging conditions. The registration of several data sources is however not trivial if the appearance of objects produced by each sensor differs substantially. This problem is further complicated when parallax effects cannot be ignored when using close-range stereo pairs. In this work, we present a new method to simultaneously tackle multispectral segmentation and stereo registration. Using an iterative procedure, we estimate the labeling result for one problem using the provisional result of the other. Our approach is based on the alternating minimization of two energy functions that are linked through the use of dynamic priors. We rely on the integration of shape and appearance cues to find proper multispectral correspondences, and to properly segment objects in low contrast regions. We also formulate our model as a frame processing pipeline using higher order terms to improve the temporal coherence of our results. Our method is evaluated under different configurations on multiple multispectral datasets, and our implementation is available online.
关键词： Multispectral imagery,Energy minimization,Cosegmentation,Video signal processing,Video object segmentation
更新于2025-09-23 15:22:29
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The Spectral-Spatial Joint Learning for Change Detection in Multispectral Imagery
摘要： Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the spectral and spatial information into account. Moreover, the underlying information of fused features is not fully explored. To address the above-mentioned problems, a Spectral-Spatial Joint Learning Network (SSJLN) is proposed. SSJLN contains three parts: spectral-spatial joint representation, feature fusion, and discrimination learning. First, the spectral-spatial joint representation is extracted from the network similar to the Siamese CNN (S-CNN). Second, the above-extracted features are fused to represent the difference information that proves to be effective for the change detection task. Third, the discrimination learning is presented to explore the underlying information of obtained fused features to better represent the discrimination. Moreover, we present a new loss function that considers both the losses of the spectral-spatial joint representation procedure and the discrimination learning procedure. The effectiveness of our proposed SSJLN is verified on four real data sets. Extensive experimental results show that our proposed SSJLN can outperform the other state-of-the-art change detection methods.
关键词： discrimination learning,feature fusion,change detection,spectral-spatial representation,multispectral imagery,Siamese CNN
更新于2025-09-19 17:15:36
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[IEEE MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM) - Los Angeles, CA, USA (2018.10.29-2018.10.31)] MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM) - On the Defense Against Adversarial Examples Beyond the Visible Spectrum
摘要： Machine learning (ML) models based on RGB images are vulnerable to adversarial attacks, representing a potential cyber threat to the user. Adversarial examples are inputs maliciously constructed to induce errors by ML systems at test time. Recently, researchers also showed that such attacks can be successfully applied at test time to ML models based on multispectral imagery, suggesting this threat is likely to extend to the hyperspectral data space as well. Military communities across the world continue to grow their investment portfolios in multispectral and hyperspectral remote sensing, while expressing their interest in machine learning based systems. This paper aims at increasing the military community's awareness of the adversarial threat and also in proposing ML training strategies and resilient solutions for state of the art artificial neural networks. Specifically, the paper introduces an adversarial detection network that explores domain specific knowledge of material response in the shortwave infrared spectrum, and a framework that jointly integrates an automatic band selection method for multispectral imagery with adversarial training and adversarial spectral rule-based detection. Experiment results show the effectiveness of the approach in an automatic semantic segmentation task using Digital Globe's WorldView-3 satellite 16-band imagery.
关键词： Adversarial Machine Learning,Multispectral Imagery,Adversarial Examples,Defenses
更新于2025-09-19 17:15:36
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High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery
摘要： Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions.
关键词： Amazon,larval source management,malaria,Anopheles darlingi,drone,multispectral imagery
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) - Portici, Italy (2019.10.24-2019.10.26)] 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) - Critical analysis of instruments and measurement techniques of the shape of trees: Terresrial Laser scanner and Structured Light scanner
摘要： Coastlines, shoals, and reefs are some of the most dynamic and constantly changing regions of the globe. The emergence of high-resolution satellites with new spectral channels, such as the WorldView-2, increases the amount of data available, thereby improving the determination of coastal management parameters. Water-leaving radiance is very difficult to determine accurately, since it is often small compared to the reflected radiance from other sources such as atmospheric and water surface scattering. Hence, the atmospheric correction has proven to be a very important step in the processing of high-resolution images for coastal applications. On the other hand, specular reflection of solar radiation on nonflat water surfaces is a serious confounding factor for bathymetry and for obtaining the seafloor albedo with high precision in shallow-water environments. This paper describes, at first, an optimal atmospheric correction model, as well as an improved algorithm for sunglint removal based on combined physical and image processing techniques. Then, using the corrected multispectral data, an efficient multichannel physics-based algorithm has been implemented, which is capable of solving through optimization the radiative transfer model of seawater for bathymetry retrieval, unmixing the water intrinsic optical properties, depth, and seafloor albedo contributions. Finally, for the mapping of benthic features, a supervised classification methodology has been implemented, combining seafloor-type normalized indexes and support vector machine techniques. Results of atmospheric correction, remote bathymetry, and benthic habitat mapping of shallow-water environments have been validated with in situ data and available bionomic profiles providing excellent accuracy.
关键词： benthic habitat mapping,Atmospheric model,high-resolution multispectral imagery,WorldView-2 (WV2),bathymetry mapping,sunglint,physical and image processing techniques
更新于2025-09-19 17:13:59
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Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed
摘要： Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.
关键词： random forest,remote sensing,forest land cover,multitemporal analysis,Sentinel 2A multispectral imagery,LiDAR
更新于2025-09-09 09:28:46