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- 摘要
- 关键词
- 实验方案
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[IEEE 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) - Xi'an, China (2019.6.12-2019.6.14)] 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) - A Non-Ohmic Normally-off GaN RB-MISHEMT Featuring a Gate-Controlled Schottky Tunnel Junction
摘要： Due to the limited number of spectral channels in multispectral remote sensing images, change information, especially the multiclass changes, may be insuf?ciently represented, resulting in inaccurate detection of changes. In this paper, we propose to use unsupervised band expansion techniques to generate arti?cial spectral and spatial bands to enhance the change representation and discrimination for change detection (CD) from multispectral images. In particular, in the proposed approach, two simple nonlinear functions, i.e., multiplication and division, are applied for spectral expansion. Multiscale morphological reconstruction is used to extend the band spatial information. The expanded band sets are then used and validated in three popular unsupervised CD techniques for solving a multiclass CD problem. Experimental results obtained on three real bitemporal multispectral remote sensing datasets con?rm the effectiveness of the proposed approach.
关键词： Change detection (CD),remote sensing,nonlinear band expansion,change vector analysis,multitemporal analysis,multispectral images,dimensionality expansion
更新于2025-09-11 14:15:04
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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 - Dense Fully Convolutional Networks for Crop Recognition from Multitemporal SAR Image Sequences
摘要： This work presents a dense fully convolutional architecture for crop type recognition from multitemporal RS images. Basically, we adapted a dense fully convolutional net to deal with stacks of multitemporal data. The proposed approach was tested upon a public dataset comprising two Sentinel-1A sequences from a tropical region in South America. We took as baseline a dense convolutional network designed for patch classification. Thematic and spatial accuracy, as well as the computational load were evaluated experimentally. The proposed architecture matched the baseline in terms of recognition rates and proved to be very efficient computationally in the inference phase.
关键词： crop type classification,Deep Learning,fully convolutional networks,SAR,multitemporal analysis
更新于2025-09-09 09:28:46
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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
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Comprehensive Remote Sensing || Multitemporal Analysis of Remotely Sensed Image Data
摘要： In the last years, a large interest has been devoted to the development of novel methodologies for multitemporal information extraction and analysis. This is demonstrated by the sharp increase in the number of papers published in the major remote sensing journals, the increased number of sessions in international conferences, and the increased number of projects related to multitemporal images and data.
关键词： change detection,unsupervised bitemporal image analysis,image time series,supervised/semi-supervised bitemporal image analysis,multitemporal analysis,remote sensing
更新于2025-09-04 15:30:14