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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Shipnet for Semantic Segmentation on VHR Maritime Imagery
摘要： For VHR maritime images, sematic segmentation is a new research hotspot and plays an important role in coast line navigation, resource management and territory protection. Without enough labeled training data, it is a challenge to separate small objects on a large scale while segment the big area clearly. To deal with it, we propose a novel ShipNet and design a weighted loss function for simultaneous sea-land segmentation and ship detection. To prove the proposed method, we also built and opened a new dataset to the community which contains VHR multiscale maritime images. Compared with the FCN and ResNet, the proposed method got much better F1 scores 85.90% for ship class and 97.54% overall accuracy. Compared with multiscale FCN, the ShipNet could obtain details results like sharp edges. Even for images with bad quality, the ShipNet could also keep robust and get good results.
关键词： CNN,ship detection,Sea-land segmentation,remote sensing image
更新于2025-09-19 17:15:36
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[IEEE 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) - Guadalajara, Jalisco, Mexico (2018.11.14-2018.11.16)] 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM) - Convolutional Neural Networks for Semantic Segmentation of Multispectral Remote Sensing Images
摘要： The recent impulse in development of artificial intelligence (AI) methodologies has simplified the application of this in multiple research areas. This simplification was not favorable before, due to the limitations in dimensionality, processing time, computational resources, among others. Working with multispectral remote sensing (RS) images, in an artificial neural network (NN) was quite complex. Due the methods used required millions of processes that took a long time to be executed and produce competitive results compared with the state of the art (SoA). Deep learning (DL) strategies have been applied to alleviate these limitations and have greatly improved the use of neural networks. Therefore, this paper presents the analysis of DL-NNs to perform semantic segmentation of multispectral RS images. Images are captured by the constellation of satellites Sentinel-2 from the European Space Agency. The objective of this research is to classify each pixel of a scene into five categories: 1-vegetation, 2-soil, 3-water, 4-clouds and 5-cloud shadows. The selection of spectral bands for the formation of input datasets for segmentation of these classes is very important. The spectral signatures of each material aid to discern among several classes. Results presented in this work, show that the AI strategy proposed offer better accuracy segmentation than other methods of the SoA in competitive processing time.
关键词： semantic segmentation,Convolutional neural networks,remote sensing,multispectral images
更新于2025-09-19 17:15:36
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A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea
摘要： In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS) was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills.
关键词： simulation,DDDAS,detection,oil spill,remote sensing
更新于2025-09-19 17:15:36
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Remote sensing estimation of the biomass of floating Ulva prolifera and analysis of the main factors driving the interannual variability of the biomass in the Yellow Sea
摘要： Since 2007, green tide blooms with Ulva prolifera as the dominant species have occurred every summer in the Yellow Sea. Biomass is a critical parameter used to describe the severity of green tide blooms. In this study, we analyzed the relationships between several indices (normalized difference vegetation index (NDVI), floating algae index (FAI), ratio vegetation index (RVI), enhanced vegetation index (EVI), ocean surface algal bloom index (OSABI), Korea Ocean Satellite Center (KOSC) approach) and the biomass per unit area of Ulva prolifera by using the in situ measurements from a water tank experiment. EVI, NDVI, and FAI showed strong exponential relationships with Ulva prolifera biomass per unit area. In order to apply the relationships to satellite remote sensing data, the impacts of the atmosphere (different aerosol optical depth at 550 nm) and mixed pixels to the relationships were analyzed. The results show that atmosphere has little effect on the relationship between EVI and Ulva prolifera biomass per unit area with R2 = 0.94 and APD (the average percentage deviation) = 19.55% when EVI is calculated from Rrc (Rayleigh-corrected reflectance), and R2 = 0.95 and APD = 17.53% when EVI is calculated from Rtoa (top-of-atmosphere reflectance). Due to the low sensitivity to the atmosphere, the EVI relationship can be directly utilized in the top-of-atmosphere (TOA) reflectance without atmospheric correction. In addition, the EVI was slightly affected by mixed pixels with the APD only increased by ~10%. The EVI relationship was then applied to a long MODIS image time series to obtain the maximal total biomass of floating Ulva prolifera in the Yellow Sea from 2007 to 2016. The results showed that the maximum and minimum total biomass occurred in 2016 (~1.17 million tons) and 2012 (~0.074 million tons), respectively. The main factors that caused the inter-annual biomass variability were analyzed. The total amount of nutrients from Sheyang River which was the largest river on the northern coast of Jiangsu Province, and Porphyra cultivation in the Radial Sand Ridges of Jiangsu Province had both strong correlation with Ulva prolifera total biomass.
关键词： Atmosphere effect,Remote sensing,Ulva prolifera,Ocean color,Biomass,EVI
更新于2025-09-19 17:15:36
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Remote sensing analysis of mangrove distribution and dynamics in Zhanjiang from 1991 to 2011
摘要： Mangrove forests provide valuable societal and ecological services and goods. However, they have been experiencing high annual rates of loss in many parts of the world. In order to evaluate a long-term wetland conservation strategy that compromises urban development with comprehensive wetland ecosystem management, remote sensing techniques were used to analyze the changing mangrove distribution in the Zhanjiang Mangrove Forest National Nature Reserve. Between 1991 and 2000, the mangrove area within the study region declined from 2 264.9 to 2 085.9 ha consistent with an annual decrease of 0.79%. However, there was an overall 34.3% increase in mangrove coverage from 2 085.9 to 2 801.8 ha between 2000 and 2011. Major causes of forest loss include local human pressures in the form of deforestation, conversion to agriculture, and natural forces such as erosion. The recent gain in mangrove forest cover is attributed to effective conservation management in the nature reserve area, including intensive mangrove plantation efforts and increased local awareness of wetland conservation.
关键词： HJ-1A,landsat TM/ETM+,conservation,mangrove,remote sensing
更新于2025-09-19 17:15:36
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Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm
摘要： Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI705, mSR, and NDVI705, which was indicated by higher R2 and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R2 values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R2 = 0.721 and RMSE = 0.540 for FD-NDNI and R2 = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.
关键词： derivative,spectral index design,hyperspectral remote sensing,algorithm optimization,crop parameter inversion
更新于2025-09-19 17:15:36
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Soil Salinity Mapping and Hydrological Drought Indices Assessment in Arid Environments Based on Remote Sensing Techniques
摘要： Vegetation indices are mostly described as crop water derivatives. Normalized Difference Vegetation Index (NDVI) is one of the oldest remote sensing applications that widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives are exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. Water Supply Vegetation Index (WSVI), Soil Adjusted Vegetation Index (SAVI), Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity level on crop water stress in arid environments. In arid environments; such as Saudi Arabia, water resources are under pressure especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater; which exceed crop water requirements in most of the cases are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat OLI-8 data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi Ad-Waser. Principal Component Analysis and Artificial Neural Network Analysis are complementary tools to understand the regression pattern of the hydrological drought indices in the designated study area.
关键词： Soil Salinity Mapping,Arid Environment,Vegetation Indices,Remote Sensing techniques
更新于2025-09-19 17:15:36
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[IEEE 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Amsterdam, Netherlands (2019.9.24-2019.9.26)] 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - A Framework For An Artificial Neural Network Enabled Single Pixel Hyperspectral Imager
摘要： Compressive Sensing enables improvement of acquisition of a variety of signals in various applications with little to no discernible loss in terms of recovered image quality. The current work proposes a signal processing framework for the acquisition and fast reconstruction of compressively sampled hyperspectral images using an artificial neural network architecture. This ANN-based approach is capable of performing a fast reconstruction by avoiding the requirement of solving a computationally intensive image-specific optimization problem. The proposed framework contributes to advance single-pixel hyperspectral imaging device methodologies, which enable a significant reduction in device mechanical complexity, imaging rate, and cost. Our experiments demonstrate that a hyperspectral image can be reconstructed using only 10% of the samples without compromising classification performance. Specifically, the results show that classification performance of the compressively sampled hyperspectral image recovered using artificial neural networks is equal or higher to that of those obtained using current scanning hyperspectral imaging platforms.
关键词： remote sensing,deep learning,hyperspectral imaging,compressive sensing
更新于2025-09-19 17:13:59
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Investigation of InAs Quantum Dot Deformation During Capping with an InGaAs Layer Using Time-resolved RHEED Measurements
摘要： Development and validation of the surface suspended sediment concentration (SSC) models derived from the surface remote-sensing reflectance spectra [Rrs (λ)] are important in satellite monitoring of estuarine and coastal waters. Seven empirical and seven semianalytical spectral reflectance models for evaluation of the surface SSC were compared with one another and with laboratory tank (one dataset) and in situ measurements (two datasets) performed in different natural waters of East China. All models were presented in the form of Rrs spectral ratios, in which wavelengths were selected from the list of NASA’s satellite sensor, MODIS unsaturated central wavelengths. A statistical analysis has been performed to find the best models and spectral ratios for remote-sensing monitoring purposes. Analysis has shown that empirical models are generally superior to the semianalytical models for solution existence, prediction accuracy, and correlation with the observed SSC values. However, all semianalytical models using the red to green spectral ratio have demonstrated approximately the same accuracy and correlation as empirical models, what provides an additional support for using more simple easily calculated empirical models. Additionally, relationships between SSC and inherent optical properties (IOPs) (absorption and backscattering coefficients) and between IOPs and Rrs (λ) provided by the semianalytical models have their own benefits for aquatic optics and remote sensing purposes.
关键词： Absorbing media,scattering parameters,water pollution,backpropagation,remote sensing
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Chengdu, China (2019.5.21-2019.5.24)] 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Designs and Applications for the Controller Parameters of the Photovoltaic System
摘要： Many economically important minerals have absorption features in the short-wave infrared (SWIR; 2000–2500 nm). Sensors which measure this part of the spectrum cannot detect the wavelength minimum of a feature at ～900 nm (F900), indicative of ferric iron mineralogy. A method based on Gaussian processes (GPs) was developed and compared with multiple linear regression (MLR) to estimate the wavelength position of F900 from SWIR data (1002–1355 nm). SWIR data with different signal-to-noise ratios were acquired from crushed rock samples by a nonimaging spectrometer and an imaging spectrometer. GP estimates of wavelength position were converted to the proportion of goethite using coefficients from a regression of the proportion of goethite determined from X-ray diffraction (XRD) on wavelength position measured directly from spectra. GP-estimated wavelength positions were within the 2-nm and ～4-nm root-mean-square error of measurements made directly from spectra for nonimaging and imaging spectrometer data, respectively. Proportions of goethite derived from these estimates were respectively within 4% and 6% of the values measured by XRD. MLR performed poorly compared to GPs when applied to data with no added noise and failed when applied to data with added noise or to imaging spectrometer data. These findings indicate that the wavelength position of F900—an indicator of ferric iron mineralogy—can be estimated from data acquired at SWIR wavelengths (1002–1355 nm). This opens up possibilities for using a single (SWIR) sensor to acquire information on ferric iron mineralogy (using F900) and other minerals with diagnostic absorptions between 1000 and 2500 nm.
关键词： geology,infrared spectroscopy,iron,image sensors,remote sensing,Gaussian processes (GPs),mining industry,Electromagnetic radiation,spectral analysis,signal processing
更新于2025-09-19 17:13:59