- 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 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 - A Novel Effective Chlorophyll Indicator for Forest Monitoring Using Worldview-3 Multispectral Reflectance
摘要： This paper explores the feasibility of deriving multispectral-based effective chlorophyll indicators (MECIs) for foliage chlorophyll concentration (CHLS) estimation. An average fusion method was applied to simulate the multispectral reflectance of the WorldView-3 sensor using hyperspectral data. With the experimental data of CHLS and predictors derived from multispectral reflectance, a series of linear regression analyses were carried out to derive appropriate models for CHLS estimation. Accuracy measures of RMSE and PRMSE were used to evaluate the model performance. Results showed that the coastal-band based MECI (MECIc) and the blue-band based MECI (MECIb) were able to achieve an RMSE of 0.5657 mg/g and 0.5943 mg/g as well as a PRMSE of 36% and 38% respectively. Using the Red edge and Yellow reflectance based NDVI (NDVIREY) as a predictor, the model can reduce uncertainty and achieve an estimation of 0.4089 mg/g and 26% for RMSE and PRMSE respectively. The prediction error made by the CHLS-NDVIREY model and the CHLS-MECI model were 11% and 60% larger than 0.38 mg/g the RMSE of hyperspectral-based CHLS-ECI model. In summary, NDVIREY was able to achieve a better prediction at around a level of 75% accuracy (1-PRMSE) and therefore is able to be an effective indicator of CHLS for forest monitoring.
关键词： climate change,hyperspectral remote sensing,Chlorophyll indicator,multispectral remote sensing,forest health
更新于2025-09-23 15:22:29
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[IEEE 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - Huhhot (2018.9.14-2018.9.16)] 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE) - A Remote Sensing Image Key Target Recognition System Design Based on Faster R-CNN
摘要： Aiming at the problem of traditional low-level recognition of key targets in remote sensing images, a method for target detection and recognition based on Faster R-CNN is proposed. Firstly, the open source remote sensing image data set NWPU VHR-10 dataset is converted into VOC 2007 format as the training sets and test sets. Secondly, according to the training set category information, the hyper-parameters of the neural network are refined, and then the training set is trained using the Faster R-CNN neural network to generate a model. Finally, this model is used to detect unknown remote sensing images and identify important targets. The simulation results show that the method has high recognition accuracy and speed, and can provide reference for recognition of the key targets of remote sensing images.
关键词： Faster R-CNN,convolution neural network,deep learning,key target recognition,remote sensing image detection
更新于2025-09-23 15:22:29
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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 - Accurate Building Detection in VHR Remote Sensing Images Using Geometric Saliency
摘要： This paper aims to address the problem of detecting buildings from remote sensing images with very high resolution (VHR). Inspired by the observation that buildings are always more distinguishable in geometries than in texture or spectral, we propose a new geometric building index (GBI) for accurate building detection, which relies on the geometric saliency of building structures. The geometric saliency of buildings is derived from a mid-level geometric representation based on meaningful junctions that can locally describe anisotropic geometrical structures of images. The resulting GBI is measured by integrating the derived geometric saliency of buildings. Experiments on three public datasets demonstrate that the proposed GBI achieves very promising performance, and meanwhile shows impressive generalization capability.
关键词： remote sensing image,geometric saliency,junction,Building detection
更新于2025-09-23 15:22:29
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[IEEE 2018 International Conference on Applied Engineering (ICAE) - Batam, Indonesia (2018.10.3-2018.10.4)] 2018 International Conference on Applied Engineering (ICAE) - An Integrated Comparative Approach to Estimating Forest Aboveground Carbon Stock Using Advanced Remote Sensing Technologies
摘要： Greenhouse gases in the atmosphere play a very important role in maintaining the temperature of the earth. Plants absorb carbon in the atmosphere in the form of CO2 which is beneficial for photosynthesis which will produce O2 into the atmosphere. By utilizing remote sensing technology and field data integration, this research aims to estimating aboveground carbon reserves in the research area. The results of this research indicate that the above ground carbon stock resulting from estimation calculations using remote sensing data and field calculations using brown allometric are 103,397 TonC / Ha with an error rate of 1,8354. This error level indicates the size of the error in the estimated value of each pixel.
关键词： Carbon Stock,Batam Island,Temperature,Remote Sensing Data,Greenhouse Gases
更新于2025-09-23 15:22:29
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Indicator-Kriging-Integrated Evidence Theory for Unsupervised Change Detection in Remotely Sensed Imagery
摘要： This study proposes a novel approach based on indicator kriging and Dempster–Shafer (DS) theory for unsupervised change detection (CD) in remote sensing images (DSK). Indicator kriging is integrated to the standard DS theory. A feature set with four difference images (DIs) providing complementary change information is initially generated. Subsequently, the mass functions for each DI are determined automatically using fuzzy logic, the four pieces of DI evidence are combined by DS theory, and a preliminary CD map is achieved. The preliminary CD map is then divided into three parts adaptively—weakly con?icting part of no change, weakly con?icting part of change, and strongly con?icting part—by calculating the evidence con?ict degree for each pixel. Finally, the pixels in the weakly con?icting parts, which have little or no con?ict, are labeled as the current class, and the pixels in the strongly con?icting part that contains misclassi?ed pixels are reclassi?ed based on indicator kriging. DSK combines the advantages of different DI features and solves the con?icting situations to a large extent. The main contributions of this study include the following: 1) introducing indicator kriging into CD to manage con?ict information during DS fusion and 2) presenting a scheme for producing DI set with complementary change information, developing a novel DSK fusion model for information fusion, and de?ning the proposed CD framework. Experimental results verify that the proposed DSK is robust and effective for CD.
关键词： unsupervised change detection (CD),remote sensing,Con?ict management,indicator kriging,Dempster–Shafer (DS) theory
更新于2025-09-23 15:22:29
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Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images
摘要： Object detection in aerial images is widely applied in many applications. In recent years, faster region convolutional neural network shows a great improvement on object detecting in natural images. Considering the size and distribution characteristic of object in remote sensing images, the region proposal network (RPN) should be changed before being adopted. In this letter, a scale adaptive proposal network (SAPNet) is proposed to improve the accuracy of multiobject detection in remote sensing images. The SAPNet consists of multilayer RPNs which are designed to generate multiscale object proposals, and a ?nal detection subnetwork in which fusion feature layer has been applied for better multiobject detection. Comparative experimental results show that the proposed SAPNet signi?cantly improves the accuracy of multiobject detection.
关键词： region proposal network (RPN),multiobject detection,remote sensing images,Convolution neural network (CNN)
更新于2025-09-23 15:22:29
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Ship detection based on squeeze excitation skip-connection path networks for optical remote sensing images
摘要： Ship detection plays a crucial role in remote sensing image processing, which has drawn great attention in recent years. A novel neural network architecture named squeeze excitation skip-connection path networks (SESPNets) is proposed. A bottom-up path is added to feature pyramid network to improve feature extraction capability, and path-level skip-connection structure is firstly proposed to enhance information flow and reduce parameter redundancy. Also, squeeze excitation module is adopted, which can adaptively recalibrate channel-wise feature responses by adding an extra branch after each shortcut path connection block. The multi-scale fused region of interest (ROI) align is then proposed to obtain more accurate and multi-scale proposals. Finally, soft-non-maximum suppression is utilized to overcome the problem of non-maximum suppression (NMS) in ship detection. As demonstrated in the experiments, it can be seen that the SESPNets model has achieved the state-of-the-art performance, which shows the effectiveness of proposed method.
关键词： Skip-connection path networks,Squeeze excitation,Ship detection,Optical remote sensing images,Deep learning
更新于2025-09-23 15:22:29
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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 - Landsat 9 Thermal Infrared Sensor 2 Characterization Plan Overview
摘要： Landsat 9 will continue the Landsat data record into its fifth decade with a near-copy build of Landsat 8 with launch scheduled for December 2020. The two instruments on Landsat 9 are Thermal Infrared Sensor-2 (TIRS-2) and Operational Land Imager-2 (OLI-2). TIRS-2 is a two-channel pushbroom imager with a 15-degree field of view that will have a 16-day measurement cadence from its nominal 705-km orbit altitude. Its carefully developed instrument performance requirements and associated characterization plan will result in stable and well-understood science-quality imagery that will be used for environmental, economic and legal applications. This paper will present a summary of the plan for TIRS-2 prelaunch characterization at the component, subsystem, and instrument level.
关键词： prelaunch characterization,calibration,Landsat 9,thermal infrared remote sensing,TIRS-2
更新于2025-09-23 15:22:29
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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 - Recent Findings on the Sentinel-L Geolocation Accuracy Using the Australian Corner Reflector Array
摘要： Synthetic Aperture Radar (SAR) satellites observe range and azimuth geometric accuracies at the low centimeter level. The accuracy of geolocation is driven by several aspects, e.g. orbit determination, SAR image processing, or atmospheric error correction. Our paper concentrates on the Sentinel-1 mission and the compensation of the platform motion effects in the geolocation, which were found to limit the best possible geolocation capabilities of Sentinel-1. The key to advance the geolocation results is the rigorous compensation of the bistatic effect in azimuth, and the correction of the Doppler-induced shifts in range. First results for Sentinel-1 at the Australian reflector array consisting of 40 Corner Reflector (CR) show consistent improvement in the geolocation (1σ) to 6 cm in range and 28 cm in azimuth for both spacecrafts when using the Interferometric Wideswath (IW) product.
关键词： synthetic aperture radar,spaceborne radar,radar remote sensing,geodesy,geolocation
更新于2025-09-23 15:22:29