IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium最新文献

Discriminative Semi-Supervised Generative Adversarial Network for Hyperspectral Anomaly Detection 判别半监督生成对抗网络用于高光谱异常检测

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323688

T. Jiang, Weiying Xie, Yunsong Li, Q. Du

引用次数: 10

Siamese Generative Adversarial Network for Change Detection Under Different Scales Siamese生成对抗网络在不同尺度下的变化检测

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323499

Mengxi Liu, Q. Shi, Penghua Liu, Cheng Wan

{"title":"Siamese Generative Adversarial Network for Change Detection Under Different Scales","authors":"Mengxi Liu, Q. Shi, Penghua Liu, Cheng Wan","doi":"10.1109/IGARSS39084.2020.9323499","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323499","url":null,"abstract":"Change detection methods based on low-resolution (LR) images with higher temporal resolution often lead to fuzzy results, while high-resolution images (HRIs) can provide more detailed information to solve this problem. However, it's hard to obtain two tiles of HRIs with high-quality for rapid change detection in actual production due to low temporal resolution and high cost. Therefore, it is necessary to explore a change detection method combing low- and high-resolution images to acquire urban change areas more accurately and quickly. In this paper, an end-to-end siamese generative adversarial network (SiamGAN) integrating a super resolution network and the siamese structure was proposed for change detection under different scales. The super-resolution network is used to reconstruct low-resolution images into high-resolution images, while the siamese structure is adopted as the classification network to detect changes. In the experiments, SiamGAN achieved an F1 of 76.06% and an IoU of 61.52% in the test set, which is respectively 5.68% and 6.92% higher than the CNN-based methods using LR images after bicubic interpolation. The results show that our proposed method can effectively overcome difference in scale between low- and high-resolution images and perform change detection more precisely and rapidly.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115654303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Cycle-Consistent Adversarial Networks 基于循环一致对抗网络的航空图像语义分割领域自适应

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323650

F. Schenkel, W. Middelmann

{"title":"Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Cycle-Consistent Adversarial Networks","authors":"F. Schenkel, W. Middelmann","doi":"10.1109/IGARSS39084.2020.9323650","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323650","url":null,"abstract":"Semantic segmentation is an important computer vision task for the analysis of aerial imagery in many remote sensing applications. Due to the large availability of data it is possible to design efficient convolutional neural network based deep learning models for this purpose. But these methods usually show a weak performance when they are applied without any modifications to data from another domain with different characteristics relating to aspects concerning the sensor or environmental influences. To improve the performance of these methods domain adaptation approaches can be employed. In the following work, we want to present a method for unsupervised domain adaptation for semantic segmentation. We trained an encoder-decoder model on the source domain dataset as task application and adjusted the network to the target domain. The adaptation process is based on a style transfer component, which is realized using a cycle-consistent adversarial network. Through a continuous adaptation of the task model we achieved a higher generalization of the network and increased the task method performance on the target domain.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115733641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Novel Global-Aware Deep Network for Road Detection of Very High Resolution Remote Sensing Imagery 一种用于超高分辨率遥感图像道路检测的新型全局感知深度网络

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323155

Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng

{"title":"A Novel Global-Aware Deep Network for Road Detection of Very High Resolution Remote Sensing Imagery","authors":"Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng","doi":"10.1109/IGARSS39084.2020.9323155","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323155","url":null,"abstract":"Road detection from very high-resolution (VHR) remote sensing imagery has great importance in a broad array of applications. However, the most advanced deep learning-based methods often produce fragmented road segments, due to the complex backgrounds of images, such as the occlusions and shadows caused by the trees and buildings, or the surrounding objects with similar textures. In this paper, the characteristics of existing models were analyzed and an effective road recognition method was explored, we found that capturing long-range dependencies helps improve road recognition. Therefore, a novel global-aware deep network (GAN) for road detection is proposed, in which the spatial-aware module (SAM) was applied to capture spatial context dependencies and the channel-aware module (CAM) was applied to capture the interchannel dependencies. Through establishing the relationships between spatial contexts and between channels, the GAN could effectively alleviate the road recognition problems, and the advantages of the proposed approach were validated on the public DeepGlobe road dataset. The experimental result demonstrates the superiority of our method.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117139392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optical and Polarimetric SAR Data Fusion Terrain Classification Using Probabilistic Feature Fusion 基于概率特征融合的光学和极化SAR数据融合地形分类

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324022

R. West, D. Yocky, Brian J. Redman, J. D. Laan, Dylan Z. Anderson

引用次数: 1

Monitoring the Changes of the Arctic Environment with the Joint Polar Satellite System (JPSS) Sounding Data Products 利用联合极轨卫星系统(JPSS)探空数据产品监测北极环境变化

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323874

Lihang Zhou

引用次数: 1

Small Object Change Detection Based on Multitask Siamese Network 基于多任务Siamese网络的小目标变化检测

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324150

Shreya Sharma, Eiji Kaneko, M. Toda

引用次数: 0

Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier 基于随机森林分类器的Sentinel-2数据云量评估

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323683

Petteri Nevavuori, T. Lipping, Nathaniel G. Narra, Petri Linna

引用次数: 1

Sparse Representation-Based Image Fusion for Multi-Source NDVI Change Detection 基于稀疏表示的图像融合多源NDVI变化检测

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324353

Mengliang Zhang, Yuerong Chen, Song Li, Xin Tian

引用次数: 2

Stacked Random Forests: More Accurate and Better Calibrated 堆叠随机森林:更准确和更好的校准

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324475

R. Hänsch

引用次数: 1