IEEE Geoscience and Remote Sensing Letters最新文献_第7页

Object-Oriented Mangrove Species Classification Using Hyperspectral Data and 3-D Siamese Residual Network 基于高光谱数据和三维暹罗残差网络的面向对象红树林物种分类

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-12-01 DOI: 10.1109/LGRS.2019.2962723

Zhi He, Q. Shi, Kai Liu, Jingjing Cao, Wen Zhan, B. Cao

{"title":"Object-Oriented Mangrove Species Classification Using Hyperspectral Data and 3-D Siamese Residual Network","authors":"Zhi He, Q. Shi, Kai Liu, Jingjing Cao, Wen Zhan, B. Cao","doi":"10.1109/LGRS.2019.2962723","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2962723","url":null,"abstract":"Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2150-2154"},"PeriodicalIF":4.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2962723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45575045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Linear Spectral Mixing Model-Guided Artificial Bee Colony Method for Endmember Generation 线性谱混合模型引导的人工蜂群末端生成方法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-12-01 DOI: 10.1109/LGRS.2019.2961502

Mingming Xu, Yan Zhang, Yanguo Fan, Yanlong Chen, Dongmei Song

引用次数: 3

Seismic Impedance Inversion Using Fully Convolutional Residual Network and Transfer Learning 利用全卷积残差网络和传递学习反演地震阻抗

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-12-01 DOI: 10.1109/LGRS.2019.2963106

Bangyu Wu, Delin Meng, Lingling Wang, Naihao Liu, Ying Wang

引用次数: 63

A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video 一种新的AMS-DAT卫星视频中移动车辆检测算法

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-11-09 DOI: 10.1109/lgrs.2020.3034677

Xu Chen, H. Sui, Jian Fang, Mingting Zhou, Chen Wu

{"title":"A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video","authors":"Xu Chen, H. Sui, Jian Fang, Mingting Zhou, Chen Wu","doi":"10.1109/lgrs.2020.3034677","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3034677","url":null,"abstract":"Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"36 1","pages":"1-5"},"PeriodicalIF":4.8,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3034677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62474385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Square-Law Detection of Exponential Targets in Weibull-Distributed Ground Clutter 威布尔分布地杂波中指数目标的平方律检测

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-07-22 DOI: 10.1109/lgrs.2020.3009304

Fernando Darío Almeida García, H. Mora, G. Fraidenraich, J. Filho

{"title":"Square-Law Detection of Exponential Targets in Weibull-Distributed Ground Clutter","authors":"Fernando Darío Almeida García, H. Mora, G. Fraidenraich, J. Filho","doi":"10.1109/lgrs.2020.3009304","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3009304","url":null,"abstract":"Modern radar systems use square-law detectors to search and track fluctuating targets embedded in Weibull-distributed ground clutter. However, the theoretical performance analysis of square-law detectors in the presence of Weibull clutter leads to cumbersome mathematical formulations. Some studies have circumvented this problem by using approximations or mathematical artifacts to simplify calculations. In this work, we derive a closed-form and exact expression for the probability of detection (PD) of a square-law detector in the presence of exponential targets and Weibull-distributed ground clutter, given in terms of the Fox H-function. Unlike previous studies, no approximations nor simplifying assumptions are made throughout our analysis. Furthermore, we derive a fast convergent series for the referred PD by exploiting the orthogonal selection of poles in Cauchy’s residue theorem. In passing, we also obtain closed-form solutions and series representations for the probability density function and the cumulative distribution function of the sum statistics that govern the output of a square-law detector. Numerical results and Monte Carlo simulations corroborate the validity of our expressions.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1956-1960"},"PeriodicalIF":4.8,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3009304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46449550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction 多元空间预测的Cokriging、神经网络和空间盲源分离

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-07-01 DOI: 10.1109/LGRS.2020.3011549

C. Muehlmann, K. Nordhausen, Mengxi Yi

引用次数: 9

GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network 基于标签噪声自适应全卷积神经网络的GIS监控建筑提取

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-01-30 DOI: 10.1109/LGRS.2019.2963065

Zenghui Zhang, Weiwei Guo, Mingjie Li, Wenxian Yu

{"title":"GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network","authors":"Zenghui Zhang, Weiwei Guo, Mingjie Li, Wenxian Yu","doi":"10.1109/LGRS.2019.2963065","DOIUrl":"https://doi.org/10.1109/LGRS.2019.2963065","url":null,"abstract":"Automatic building extraction from aerial or satellite images is a dense pixel prediction task for many applications. It demands a large number of clean label data to train a deep neural network for building extraction. But it is labor expensive to collect such pixel-wise annotated data manually. Fortunately, the building footprint data of geographic information system (GIS) maps provide a cheap way of generating building label data, but these labels are imperfect due to misalignment between the GIS maps and images. In this letter, we consider the task of learning a deep neural network to label images pixel-wise from such noisy label data for building extraction. To this end, we propose a general label noise-adaptive (NA) neural network framework consisting of a base network followed by an additional probability transition modular (PTM) which is introduced to capture the relationship between the true label and the noisy label. The parameters of the PTM can be estimated as part of the training process of the whole network by the off-the-shelf backpropagation algorithm. We conduct experiments on real-world data set to demonstrate that our proposed PTM can better handle noisy labels and improve the performance of convolutional neural networks (CNNs) trained on the noisy label data generated by GIS maps for building extraction. The experimental results indicate that being armed with our proposed PTM for fully CNN, it provides a promising solution to reduce manual annotation effort for the labor-expensive object extraction tasks from remote sensing images.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"17 1","pages":"2135-2139"},"PeriodicalIF":4.8,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2963065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46085301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

Introducing IEEE Collabratec IEEE Collabratec简介

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-11-01 DOI: 10.1109/lgrs.2019.2950117

引用次数: 0

Aspirin inhibits proliferation and promotes apoptosis of hepatocellular carcinoma cells via wnt/β-catenin signaling pathway. 阿司匹林通过 wnt/β-catenin 信号通路抑制肝癌细胞增殖并促进其凋亡。

IF 4.3 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-10-24 DOI: 10.23736/S0031-0808.19.03722-4

Jianhua Sun, Chenyu Guo, Wenwen Zheng, Xuelin Zhang

引用次数: 0

Corn Plant Counting Using Deep Learning and UAV Images 利用深度学习和无人机图像进行玉米植株计数

IF 4.8 3区地球科学

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-08-08 DOI: 10.1109/LGRS.2019.2930549

Bruno T. Kitano, C. Mendes, A. R. Geus, Henrique C. Oliveira, Jefferson R. Souza

引用次数: 65