- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Remote Sensing: An Automated Methodology for Olive Tree Detection and Counting in Satellite Images
摘要： Cultivation of olive trees for the past few years has been widely spread across Mediterranean countries, including Spain, Greece, Italy, France, and Turkey. Among these countries, Spain is listed as the largest olive producing country with almost 45% of olive oil production per year. Dedicating land of over 2.4 million hectares for the olive cultivation, Spain is among the leading distributors of olives throughout the world. Due to its high signi?cance in the country’s economy, the crop yield must be recorded. Manual collection of data over such expanded ?elds is humanly infeasible. Remote collection of such information can be made possible through the utilization of satellite imagery. This paper presents an automated olive tree counting method based on image processing of satellite imagery. The images are pre-processed using the unsharp masking followed by improved multi-level thresholding-based segmentation. Resulting circular blobs are detected through the circular Hough transform for identi?cation. Validation has been performed by evaluating the proposed scheme for the dataset formed by acquiring images through the ‘‘El Sistema de Información Geográ?ca de Parcelas Agrícolas’’ viewer over the region of Spain. The proposed algorithm achieves an accuracy of 96% in detection. Computation time was recorded as 24 ms for an image size of 300 × 300 pixels. The less spectral information is used in our proposed methodology resulting in a competitive accuracy with low computational cost in comparison to the state-of-the-art technique.
关键词： crop estimation,multi-spectral imagery,Remote sensing,olive,Hough transform,satellite imagery
更新于2025-09-23 15:23:52
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[IEEE NAECON 2018 - IEEE National Aerospace and Electronics Conference - Dayton, OH, USA (2018.7.23-2018.7.26)] NAECON 2018 - IEEE National Aerospace and Electronics Conference - Onboard Image Processing for Small Satellites
摘要： In general, the computational ability of spacecraft and satellites has lagged behind terrestrial computers by several generations. Moore's Law turns the supercomputers of yesterday into the laptops of today, but space computing remains relatively underpowered due to the harsh radiation environment and low risk-tolerance of most space missions. Space missions are generally low risk because of the high cost of components and launch. However, launch costs are drastically decreasing and innovations such as CubeSats are changing the risk equation. By accepting more risk and utilizing commercial of the shelf (COTS) parts, it is possible to cheaply build and launch extremely capable computing platforms into space. High performance satellites will be required for advanced interplanetary exploration due to latency challenges. The long transmission times between planets means satellites or robotic explorers need onboard processing to perform tasks in real-time. This paper explores one possible application that could be hosted onboard the next generation of high performance satellites, performing object classification on satellite imagery. Automation of satellite imagery processing is currently performed by servers or workstations on Earth, but this paper will show that those algorithms can be moved onboard satellites by using COTS components. First traditional computer vision techniques such as edge detection and sliding windows are used to detect possible objects on the open ocean. Then a modern neural network architecture is used to classify the object as a ship or not. This application is implemented on a Nvidia Jetson TX2 and measurements of the application's power use confirm that it fits within the Size Weight and Power (SWAP) requirements of SmallSats and possibly even CubeSats.
关键词： Satellite Imagery,Machine Learning,Neural Networks,Onboard Processing
更新于2025-09-23 15:22:29
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A Novel Approach for Seamless Probabilistic Photovoltaic Power Forecasting Covering Multiple Time Frames
摘要： Uncertainty in the upcoming production of photovoltaic (PV) plants is a challenge for grid operations and also a source of revenue loss for PV plant operators participating in electricity markets, since they have to pay penalties for the mismatch between contracted and actual productions. Improving PV predictability is an area of intense research. In real-world applications, forecasts are often needed for different time frames (horizon, update frequency, etc.) and are derived by dedicated models for each time frame (i.e. for day ahead and for intra-day trading). This can result in both different forecasted values corresponding to the same horizon and discontinuities among time-frames. In this paper we address this problem by proposing a novel seamless probabilistic forecasting approach able to cover multiple time frames. It is based on the Analog Ensemble (AnEn) model, however it is adapted to consider the most appropriate input for each horizon from a pool of available input data. It is designed to be able to start at any time of day, for any forecast horizon, making it well-suited for applications like continuous trading. It is easy to maintain as it adapts to the latest data and does not need regular retraining. We enhance short-term predictability by considering data from satellite images and in situ measurements. The proposed model has low complexity compared to benchmark models and is trivially parallelizable. It achieves performance comparable to state-of-the-art models developed speci?cally for the short term (i.e. up to 6 hours) and the day ahead. The evaluation was carried out on a real-world case comprising three PV plants in France, over a period of one year.
关键词： Probabilistic Forecasting,Satellite Imagery,Photovoltaics,Analog-Ensemble Model
更新于2025-09-16 10:30:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Image Translation Between Sar and Optical Imagery with Generative Adversarial Nets
摘要： In this paper, we propose a method for the translation from Synthetic Aperture Radar (SAR) to optical images using conditional Generative Adversarial Networks (cGANs). Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds leads to unstable monitoring of the ground situation while using the optical camera. Images captured by a longer wavelength are introduced to reduce the effects of clouds. In particular, SAR images are known to be nearly unaffected by clouds and are often used for stably observing the ground situation. On the other hand, SAR images have lower spatial resolution and visibility than optical images. Therefore, we propose a deep neural network that generates optical images from SAR images. Finally, we confirm the feasibility of the proposed network on a dataset consisting of optical images and the corresponding SAR images.
关键词： SAR,Deep learning,GANs,Satellite imagery
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Predicting Landscapes as Seen from Space from Environmental Conditions
摘要： Satellite images are information rich snapshots of ecosystems and landscapes. In consequence, the features in the images strongly depend on the environmental conditions. Such dependency between climate and landscapes has been regarded since the beginning of earth sciences; however, it has never been taken as literally as in the present study. We adapted a deep learning generative model as a first demonstration of the potential behind deep learning for spatial pattern generation in geoscience. The purpose is to build a conditional Generative Adversarial Network (cGAN) useful to establish the relationship between two loosely linked set of variables that show multitude of complex spatial features such as climate conditions to aerial image. We trained a custom cGAN to generate Sentinel-2 multispectral imagery given a set of climatic and terrain predictors. Results show that the generated imagery shares many characteristics with the real one. In some cases, the quality of the generated imagery is high enough to deceive humans. We envision that such use of deep learning for geoscience could become an important tool to test the effects of climate on landscapes and ecosystems.
关键词： GAN,climate,deep learning,satellite imagery,Sentinel 2,landscape ecology
更新于2025-09-10 09:29:36
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Estimation of forest structural and compositional variables using ALS data and multi-seasonal satellite imagery
摘要： Advanced forest resource inventory (FRI) information is of critical importance for sustainable forest management. FRIs are dependent on remote sensing data and processing methods, along with field calibration/validation to generate cost-effective options for modelling forest inventory and biophysical variables over large areas. The objective of this study was to examine the impact of combining multi-seasonal multispectral satellite imagery with airborne laser scanning (ALS) data for estimating basal area, species mixture and stem density for an uneven-aged tolerant hardwood forest in Ontario, Canada. Using random forest (RF) regression as a non-parametric diagnostic technique, three multispectral optical sensors (i.e., Landsat-5 TM, Sentinel-2 A and WorldView-2) were compared to examine the most cost-effective sensor configuration for modelling FRI variables. The contribution of spectral predictors derived from these optical sensors as well as ALS height and intensity metrics were evaluated using RF variable importance. As part of our variable selection framework, all predictor variables were grouped into relatively independent clusters using a hierarchical variable clustering technique, which revealed the distinctiveness between information contained in spectral predictors, height- and intensity-based metrics. This indicates that ALS intensity data carry unique information complementary to passive near-infrared data for forest characterization. ALS data alone did not result in accurate models for basal area and species mixture, but predictive accuracies were improved significantly with the addition of spectral predictors. Compared to single-date images, multi-seasonal imagery proved to be more accurate for modelling FRI variables, especially when combined with ALS data. Despite its limited spatial resolution, Sentinel-2 A was found to be the most cost-effective image source for enhancing ALS-based FRI models. Using variables identified by the variable selection procedure, best subsets regression outperformed the RF models developed for diagnostic analysis, resulting in a suite of accurate and parsimonious predictive models, with coefficients of determination of 0.73, 0.90 and 0.67, for basal area, species mixture, and stem density, respectively.
关键词： Multi-seasonal satellite imagery,Variable selection,Sentinel-2A,Airborne laser scanning (ALS),Forest resource inventory
更新于2025-09-10 09:29:36
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PEMANFAATAN CITRA SATELIT UNTUK PENENTUAN LAHAN KRITIS MANGROVE DI KECAMATAN TUGU, KOTA SEMARANG
摘要： This study aims to mapping the level of degraded land of mangrove forest area In TUGU Sub-district, Semarang, by comparing the results between the Landsat 7 ETM + images of 2009 and ALOS AVNIR-2 in 2009. In determining the degradation of mangrove forest area, we used geographic information systems and remote sensing as a tool of analysis that is based on three (3) criteria; land use type, canopy density, and soil resilience from abrasion. From 2 satellite image data used, it will be supervised image classification using ER Mapper software to get the criteria type of land use and density of the canopy. For soil resilience from abrasion, we used soil types reclassification techniques, using ArcGIS software. Based on Landsat imagery, obtained results 92.22 % of mangrove forest area included in severely damaged condition and 7.78% is included in the category of moderate damage. Meanwhile, based on the results of ALOS image, 77.73 % of mangrove areas in severely damaged condition and 22.27 % are included in the category of moderate damage. From this study, it can be concluded that ALOS and Landsat Imagery is good for the determination and identifying critical mangrove area and distribution of mangrove forests, but the degraded land of mangrove maps generated by Landsat, less detailed than ALOS in classification and representation the conditions of critical mangrove area in Tugu sub-district.
关键词： Satellite Imagery,GIS,Mangrove,Critical Land,Remote Sensing
更新于2025-09-04 15:30:14
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[IEEE 2018 Conference Grid, Cloud & High Performance Computing in Science (ROLCG) - Cluj-Napoca, Romania (2018.10.17-2018.10.19)] 2018 Conference Grid, Cloud & High Performance Computing in Science (ROLCG) - Efficient search algorithms implementation for SAR image analysis
摘要： Earth Observation (EO) through remote sensing satellites gathers reliable information about the environment. The evolution of satellite instrumentation leads to considerable satellite data volumes which require on one hand, mathematically and statistically based algorithms to extract valuable information and, on the other hand, increased computational power for processing. The paper proposes image processing algorithms for Synthetic Aperture Radar (SAR) images together with application-specific hardware architectures for satellite imagery. It is well known that performing feature extraction in image analysis involves complex search algorithms implementation. A novel approach based on content-addressable memories (CAM) for search algorithms optimization is proposed. Results in terms of computational time are presented for the proposed approach and compared with classical image processing search algorithms implementations.
关键词： satellite imagery,search algorithms,hardware implementation,content addressable memories
更新于2025-09-04 15:30:14