2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)最新文献_第3页

Mining Mid-Level Visual Elements for Object Detection in High-Resolution Remote Sensing Images 面向高分辨率遥感图像目标检测的中层视觉元素挖掘

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486179

Xinle Liu, Hui-bin Yan, H. Huo, T. Fang

引用次数: 0

Denoising of Hyperspectral Images Based on Principal Component Analysis and Adaptive Sparse Coding 基于主成分分析和自适应稀疏编码的高光谱图像去噪

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486272

Song Xiaorui, Wu Lingda

{"title":"Denoising of Hyperspectral Images Based on Principal Component Analysis and Adaptive Sparse Coding","authors":"Song Xiaorui, Wu Lingda","doi":"10.1109/PRRS.2018.8486272","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486272","url":null,"abstract":"In view of the special properties of hyperspectral images(HSI) in the transform domain, in this paper, a new denoising method of HSI based on principal component analysis(PCA) and adaptive sparse coding is proposed. Firstly, the principal component image of each channel is obtained by performing PCA transform on the noisy HSI. Then, the first PCA output channels which contain a majority of the total energy of an HSI data cube are retained, and the rest PCA output channels which contain a small amount of energy, termed noise component images, are subjected to noise reduction through an adaptive sparse coding method. The encoding dictionaries are learned from each channel of noise component images by an approach of online dictionary learning. Finally, the denoised HSI is obtained by the inverse PCA transform. The proposed method takes the advantages of PCA and adaptive sparse representation that has better adaptability to the HSI. It not only performs better in denoising, but also preserves the details and alleviates the blocking artifacts well. The effectiveness of the proposed approach to hyperspectral denoising, termed PCASpC, is illustrated in a series of experiments with synthetic and realworld data where it outperforms the state-of-the-art.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125801428","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: 2

An Image Matching Correction Method of Integrating Least Squares and Phase Correlation Using Window Series 基于窗口序列的最小二乘与相位相关积分图像匹配校正方法

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486359

Song Wenping, Niu Changling

{"title":"An Image Matching Correction Method of Integrating Least Squares and Phase Correlation Using Window Series","authors":"Song Wenping, Niu Changling","doi":"10.1109/PRRS.2018.8486359","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486359","url":null,"abstract":"Matching is the knotty point in photogrammetry and computer vision. Aiming at inaccurate corresponding points after preliminary matching, this paper proposed an image matching correction method of integrating least squares and phase correlation using window series. The method firstly uses least squares and phase correlation matching to correct corresponding points in utilizing of window series, and simultaneously calculates correlation coefficients using windows of different size. And then the correlation coefficients are used as the index of evaluating whether the corresponding image points are accurate or not. So the matching results with the largest correlation coefficients are chosen as the final results. Based on experimental data-set 1 and data-set 2, the experimental results revealed that the use of window series can significantly improve the correction accuracy of preliminary matching results. And the proposed method can correct the corresponding points of preliminary matching effectively and greatly improve the overall matching accuracy, which is better than least squares matching or phase correlation matching using window series and fixed windows.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124424153","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: 1

Inversion of Heavy Metal Content in a Copper Mining Area Based on Extreme Learning Machine Optimized by Particle Swarm Algorithm 基于粒子群算法优化极限学习机的铜矿区重金属含量反演

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486172

Xinyue Zhang, X. Niu, Fengyan Wang, Xu Zeshuang, Xuqing Zhang, Shengbo Chen, Mingchang Wang

引用次数: 0

Building Change Detection Based on Multi-Scale Filtering and Grid Partition 基于多尺度滤波和网格划分的建筑物变化检测

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486194

Qi Bi, K. Qin, Han Zhang, Wenjun Han, Zhili Li, Kai Xu

引用次数: 2

Road Map Update from Satellite Images by Object Segmentation and Change Analysis 基于目标分割和变化分析的卫星图像路线图更新

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486330

Xia Wei, Sun Shikai, L. Jian

引用次数: 2

An Improved Simplex Maximum Distance Algorithm for Endmember Extraction in Hyperspectral Image 一种改进的单纯形最大距离算法用于高光谱图像端元提取

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486201

Qian Wang, Pengfei Liu, Lifu Zhang

{"title":"An Improved Simplex Maximum Distance Algorithm for Endmember Extraction in Hyperspectral Image","authors":"Qian Wang, Pengfei Liu, Lifu Zhang","doi":"10.1109/PRRS.2018.8486201","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486201","url":null,"abstract":"Simplex maximum distance (SMD) is an algorithm based on that the pixel with the biggest distance from simplex formed by known endmembers is most likely to be the next endmember. However, SMD involves calculation of some intermediate variables, such as simplex's normal vector, and intersection point of simplex and line, leading to computation complexity. In addition, high brightness points, outliers and isolated noise points in hyperspectral image are often extracted as endmembers in SMD. To overcome these two shortages, an improved simplex maximum distance (ISMD) algorithm is presented in the paper. To simplify computation procedure, ISMD defines the distance from pixel to simplex as ratio of volumes of parallel polyhedrons with adjacent dimensions. Once distances of all pixels from existing simplex are received, a set of similar pixels was selected from multiple pixels with a larger distance according to the spectral angle. The set of pixels is averaged to be the new endmember. The ISMD algorithm was assessed using simulated and real AVIRIS images. Compared with SMD, ISMD better extracted real endmembers in simulated image. And spectral angle between endmember obtained by ISMD and corresponding mineral from USGS spectral library is less for AVIRIS image.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114884422","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

Deep Cross-Modal Retrieval for Remote Sensing Image and Audio 遥感图像和音频的深度跨模态检索

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486338

Gou Mao, Yuan Yuan, Lu Xiaoqiang

{"title":"Deep Cross-Modal Retrieval for Remote Sensing Image and Audio","authors":"Gou Mao, Yuan Yuan, Lu Xiaoqiang","doi":"10.1109/PRRS.2018.8486338","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486338","url":null,"abstract":"Remote sensing image retrieval has many important applications in civilian and military fields, such as disaster monitoring and target detecting. However, the existing research on image retrieval, mainly including to two directions, text based and content based, cannot meet the rapid and convenient needs of some special applications and emergency scenes. Based on text, the retrieval is limited by keyboard inputting because of its lower efficiency for some urgent situations and based on content, it needs an example image as reference, which usually does not exist. Yet speech, as a direct, natural and efficient human-machine interactive way, can make up these shortcomings. Hence, a novel cross-modal retrieval method for remote sensing image and spoken audio is proposed in this paper. We first build a large-scale remote sensing image dataset with plenty of manual annotated spoken audio captions for the cross-modal retrieval task. Then a Deep Visual-Audio Network is designed to directly learn the correspondence of image and audio. And this model integrates feature extracting and multi-modal learning into the same network. Experiments on the proposed dataset verify the effectiveness of our approach and prove that it is feasible for speech-to-image retrieval.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121948353","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: 43

Dense Cloud Classification on Multispectral Satellite Imagery 基于多光谱卫星图像的稠密云分类

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486379

K. Wohlfarth, C. Schröer, Maximilian Klass, Simon Hakenes, Maike Venhaus, S. Kauffmann, T. Wilhelm, C. Wohler

引用次数: 4

FPGA Optimization for Hyperspectral Target Detection with Collaborative Representation 基于协同表示的高光谱目标检测FPGA优化

2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) Pub Date : 2018-08-01 DOI: 10.1109/PRRS.2018.8486378

Peidi Yang, Wei Li, Xuebin Li, Lianru Gao

{"title":"FPGA Optimization for Hyperspectral Target Detection with Collaborative Representation","authors":"Peidi Yang, Wei Li, Xuebin Li, Lianru Gao","doi":"10.1109/PRRS.2018.8486378","DOIUrl":"https://doi.org/10.1109/PRRS.2018.8486378","url":null,"abstract":"Currently, remote sensing image processing raises much higher requirements on the computing platform and processing speed. The high speed, low power, reconfigurable and radiation resistance features of Field Programmable Gate Arrays (FPGA) makes it become a better choice for real-time processing in hyperspectral imagery. In this paper, we have optimized the newly proposed hyperspectral target detection algorithm based on FPGA. The collaborative representation is a high-efficiency target detection (CRD) algorithm in hyperspectral imagery, which is directly based on the concept that the target pixels can be approximately represented by its spectral signatures, while the other cannot. Using the Sherman-Morrison formula to calculate the matrix inversion and the difficulty of implementing the overall CRD algorithm on the FPGA is reduced. The running speed of parallel programming is greatly promoted on the FPGA under the premise of reasonable resources. The experimental results demonstrate that the proposed system has significantly improved the processing time when compared to the pre-optimized system and the 3.40 GHzCPU.","PeriodicalId":197319,"journal":{"name":"2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126605185","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: 1