Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7325856
Hui Lu, T. Koike
The spatial distribution characteristics and temporal variation trends of soil moisture significantly affect terrestrial water, energy, and carbon cycles at various scales. Satellite remote sensing is highly expected to provide such valuable information. Before applying the remotely sensed soil moisture products, a thorough validation must be conducted to insure product quality. In this paper, we evaluate the soil moisture products retrieved from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission - Water (GCOM-W) over Heihe river basin in China, respectively. The land cover in Heihe river basin changes from desert to grass, agriculture field, and then mountain forest, which makes the basin an obvious spatial variation in soil moisture field and valuable to check the reliability and stability of two soil moisture products. We calculate the diurnal relative difference (DRD) of monthly averaged soil moisture between day and night observation of each products, and comparing them with that calculated from corresponding Global Land Data Assimilation System (GLDAS) simulations. The comparison results indicate that the SMOS soil moisture products are much unstable than AMSR2 retrievals. The DRD of SMOS is 20 times larger than that of AMSR2 and 100 times larger than that of GLDAS. We speculate that the radio frequency interference effects on SMOS observation may contribute to this unstable performance. Moreover, the retrievals from multi-angle observations in SMOS algorithm is also a potential source causing this systemic bias.
{"title":"Evaluation of AMSR2 and SMOS soil moisture products over Heihe river basin in China","authors":"Hui Lu, T. Koike","doi":"10.1109/IGARSS.2015.7325856","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325856","url":null,"abstract":"The spatial distribution characteristics and temporal variation trends of soil moisture significantly affect terrestrial water, energy, and carbon cycles at various scales. Satellite remote sensing is highly expected to provide such valuable information. Before applying the remotely sensed soil moisture products, a thorough validation must be conducted to insure product quality. In this paper, we evaluate the soil moisture products retrieved from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission - Water (GCOM-W) over Heihe river basin in China, respectively. The land cover in Heihe river basin changes from desert to grass, agriculture field, and then mountain forest, which makes the basin an obvious spatial variation in soil moisture field and valuable to check the reliability and stability of two soil moisture products. We calculate the diurnal relative difference (DRD) of monthly averaged soil moisture between day and night observation of each products, and comparing them with that calculated from corresponding Global Land Data Assimilation System (GLDAS) simulations. The comparison results indicate that the SMOS soil moisture products are much unstable than AMSR2 retrievals. The DRD of SMOS is 20 times larger than that of AMSR2 and 100 times larger than that of GLDAS. We speculate that the radio frequency interference effects on SMOS observation may contribute to this unstable performance. Moreover, the retrievals from multi-angle observations in SMOS algorithm is also a potential source causing this systemic bias.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125915449","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326877
Rohan Kumar, R. Anbalagan
Landslide susceptibility mapping is necessary in order to facilitate rational, systematic and efficient decisions concerning planning of development in mountainous regions and also for the mitigation and management of landslide disasters. Radial Basis Function Link Networks (RBFLN) was used as a landslide inventory-driven method for the identification of landslide susceptibility. Generation of input data for RBFLN involved the landslide causal factor (evidential theme) maps comprising geology, photo-lineament, land use land cover (LULC), soil, slope angle, aspect, relative relief, profile curvature, distance to drainage and distance to reservoir boundary. 116 landslide incidence and 116 no incidences were used to train the network. A unique condition grid map was prepared by the combination of each evidential theme. For each input training vector, weights in the form of fuzzy membership function were assigned. Based on fuzzy membership values, weights of each pixel of unique condition grid map were computed on the basis of RBFLN. The RBFLN weights were linked to the unique condition grid and a continuous landslide prediction map was created which was further classified into five relative susceptible zones.
{"title":"Remote sensing and GIS based artificial neural network system for landslide suceptibility mapping","authors":"Rohan Kumar, R. Anbalagan","doi":"10.1109/IGARSS.2015.7326877","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326877","url":null,"abstract":"Landslide susceptibility mapping is necessary in order to facilitate rational, systematic and efficient decisions concerning planning of development in mountainous regions and also for the mitigation and management of landslide disasters. Radial Basis Function Link Networks (RBFLN) was used as a landslide inventory-driven method for the identification of landslide susceptibility. Generation of input data for RBFLN involved the landslide causal factor (evidential theme) maps comprising geology, photo-lineament, land use land cover (LULC), soil, slope angle, aspect, relative relief, profile curvature, distance to drainage and distance to reservoir boundary. 116 landslide incidence and 116 no incidences were used to train the network. A unique condition grid map was prepared by the combination of each evidential theme. For each input training vector, weights in the form of fuzzy membership function were assigned. Based on fuzzy membership values, weights of each pixel of unique condition grid map were computed on the basis of RBFLN. The RBFLN weights were linked to the unique condition grid and a continuous landslide prediction map was created which was further classified into five relative susceptible zones.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125934805","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326345
Gabriele Cavallaro, M. Mura, N. Falco, J. Benediktsson
Attribute profiles (APs) have increasingly been receiving more attention over the last years, as they are able to extract and model spatial information that is useful for the analysis of remote sensing images of very high spatial resolution (VHR). However, one of the major issues in employing APs is the choice of a proper range of thresholds, able to provide a representative and non-redundant multi-level image decomposition. This paper presents a novel method for the automatic selection of adequate thresholds to compute the AP. A new concept of cumulative function, which can be seen as an extension of the basic notion of granulometry, is introduced. In particular, different information on the spatial context is achieved according to the measure used for computing the cumulative function, which is computed on the AP composed by considering all possible values of the attribute. The proposed approach aims at selecting the set of thresholds that provides the best approximation of the resulting cumulative function based on the chosen measure. Experimental analysis carried out on a very high resolution image shows the effectiveness of the presented strategy in providing a set of thresholds able to retain the salient spatial structures in the scene.
{"title":"Automatic morphological attribute profiles","authors":"Gabriele Cavallaro, M. Mura, N. Falco, J. Benediktsson","doi":"10.1109/IGARSS.2015.7326345","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326345","url":null,"abstract":"Attribute profiles (APs) have increasingly been receiving more attention over the last years, as they are able to extract and model spatial information that is useful for the analysis of remote sensing images of very high spatial resolution (VHR). However, one of the major issues in employing APs is the choice of a proper range of thresholds, able to provide a representative and non-redundant multi-level image decomposition. This paper presents a novel method for the automatic selection of adequate thresholds to compute the AP. A new concept of cumulative function, which can be seen as an extension of the basic notion of granulometry, is introduced. In particular, different information on the spatial context is achieved according to the measure used for computing the cumulative function, which is computed on the AP composed by considering all possible values of the attribute. The proposed approach aims at selecting the set of thresholds that provides the best approximation of the resulting cumulative function based on the chosen measure. Experimental analysis carried out on a very high resolution image shows the effectiveness of the presented strategy in providing a set of thresholds able to retain the salient spatial structures in the scene.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126061732","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7325692
G. Vivone, R. Restaino, M. Mura, J. Chanussot
Pansharpening consists of fusing a multispectral (MS) image together with a panchromatic (PAN) image with the aim of jointly preserving the spectral diversity of the former and the geometric richness of the latter. A crucial step in pansharpening algorithms is the detail extraction. This problem is usually addressed by the means of 2D Gaussian filters matched with the MS sensor's modulation transfer function (MTF). Nevertheless, several issues can affect this characterization (e.g. the MTF's gains at the Nyquist frequency could be not available or unreliable). Thus, in this paper we propose a technique based on blind image deblurring in order to estimate band-dependent spatial detail extraction filters by taking into consideration the possible variability of the MS spatial features along bands. The validation is carried out exploiting two real datasets acquired by the IKONOS and the QuickBird sensors.
{"title":"Multi-band semiblind deconvolution for pansharpening applications","authors":"G. Vivone, R. Restaino, M. Mura, J. Chanussot","doi":"10.1109/IGARSS.2015.7325692","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325692","url":null,"abstract":"Pansharpening consists of fusing a multispectral (MS) image together with a panchromatic (PAN) image with the aim of jointly preserving the spectral diversity of the former and the geometric richness of the latter. A crucial step in pansharpening algorithms is the detail extraction. This problem is usually addressed by the means of 2D Gaussian filters matched with the MS sensor's modulation transfer function (MTF). Nevertheless, several issues can affect this characterization (e.g. the MTF's gains at the Nyquist frequency could be not available or unreliable). Thus, in this paper we propose a technique based on blind image deblurring in order to estimate band-dependent spatial detail extraction filters by taking into consideration the possible variability of the MS spatial features along bands. The validation is carried out exploiting two real datasets acquired by the IKONOS and the QuickBird sensors.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126152793","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7325696
Maria Angeles Garcia-Sopo, A. Cuartero, P. G. Rodríguez, A. Plaza
Light Detection and Ranging (LiDAR) is a technology used in different topic (mapping, urban land cover, agriculture, forestry, etc.). The great potential of LiDAR data lies in its high accuracy in the measurement of heights. Hyperspectral images, which comprise hundreds of (nearly contiguous) spectral channels, can also have spatial resolution of up to 1-5 meters per pixel. In this work, we propose to integrate both hyperspectral and LiDAR data by adding the LiDAR information to the hyperspectral data cube and correcting the geometric distortions. After arranging both data sets in the same format, we analyzed the errors obtained for each data source in order to determine if the final resolution adopted was the most appropriate one for performing data fusion. Our experimental results, in an area of Extremadura, indicate improvements in the classification after integrating the hyperspectral and LiDAR data.
{"title":"Hyperspectral and lidar data integration and classification","authors":"Maria Angeles Garcia-Sopo, A. Cuartero, P. G. Rodríguez, A. Plaza","doi":"10.1109/IGARSS.2015.7325696","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325696","url":null,"abstract":"Light Detection and Ranging (LiDAR) is a technology used in different topic (mapping, urban land cover, agriculture, forestry, etc.). The great potential of LiDAR data lies in its high accuracy in the measurement of heights. Hyperspectral images, which comprise hundreds of (nearly contiguous) spectral channels, can also have spatial resolution of up to 1-5 meters per pixel. In this work, we propose to integrate both hyperspectral and LiDAR data by adding the LiDAR information to the hyperspectral data cube and correcting the geometric distortions. After arranging both data sets in the same format, we analyzed the errors obtained for each data source in order to determine if the final resolution adopted was the most appropriate one for performing data fusion. Our experimental results, in an area of Extremadura, indicate improvements in the classification after integrating the hyperspectral and LiDAR data.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123326292","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}
The angular superresolution is of great significance for scanning radar in forward-looking imaging. There are many techniques documented in literature to enhance the angular resolution, of which deconvolution method and power spectral density(PSD)methods are favored and attain many interests. In this paper, we focus on analyzing the advantages and challenges of PSD methods in comparison with the deconvolution method. Firstly, three typical PSD estimation approaches are introduced, followed with the comparison with deconvo-lution method that summarizes the advantages and challenges of PSD methods in theory. Simulations are provided in terms of coherence and number of snapshots, which presents the performance of different PSD methods and Lucy-Richardson deconvolution method, better demonstrating the advantages and challenges of PSD methods.
{"title":"Advantages and challenges of power spectral density estimation methods for scanning radar angular superresolution","authors":"Yue Wang, Yongchao Zhang, Yulin Huang, Wenchao Li, Jianyu Yang, Haiguang Yang","doi":"10.1109/IGARSS.2015.7326171","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326171","url":null,"abstract":"The angular superresolution is of great significance for scanning radar in forward-looking imaging. There are many techniques documented in literature to enhance the angular resolution, of which deconvolution method and power spectral density(PSD)methods are favored and attain many interests. In this paper, we focus on analyzing the advantages and challenges of PSD methods in comparison with the deconvolution method. Firstly, three typical PSD estimation approaches are introduced, followed with the comparison with deconvo-lution method that summarizes the advantages and challenges of PSD methods in theory. Simulations are provided in terms of coherence and number of snapshots, which presents the performance of different PSD methods and Lucy-Richardson deconvolution method, better demonstrating the advantages and challenges of PSD methods.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126617134","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326966
Jaime Delgado, G. Martín, J. Plaza, L. Jimenez, A. Plaza
The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics of the hyperspectral data. Particularly, spatial preprocessing algorithm (SPP) consists on a preprocessing technique that can be used prior to most of existing spectral-based endmember extraction process, thus promoting the selection of endmem-bers from the most spatially homogeneous regions of the data set. This paper presents a parallel implementation of SPP algorithm which is tested over two different graphic processing units (GPUs) architectures: NVidiaTMGeForce GTX 580 and NVidiaTMGeForce GTX 870M. Experimental validation using a hyperspectral data set collected by AVIRIS sensor shows that it is possible to achieve real-time performance.
{"title":"GPU implementation of spatial preprocessing for spectral unmixing of hyperspectral data","authors":"Jaime Delgado, G. Martín, J. Plaza, L. Jimenez, A. Plaza","doi":"10.1109/IGARSS.2015.7326966","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326966","url":null,"abstract":"The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics of the hyperspectral data. Particularly, spatial preprocessing algorithm (SPP) consists on a preprocessing technique that can be used prior to most of existing spectral-based endmember extraction process, thus promoting the selection of endmem-bers from the most spatially homogeneous regions of the data set. This paper presents a parallel implementation of SPP algorithm which is tested over two different graphic processing units (GPUs) architectures: NVidiaTMGeForce GTX 580 and NVidiaTMGeForce GTX 870M. Experimental validation using a hyperspectral data set collected by AVIRIS sensor shows that it is possible to achieve real-time performance.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115018633","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326453
Jin-King Liu, K. Chang, Chinsu Lin, Liang-Cheng Chang
Recently, some global-scale DEM products, such as GTOPO30, ETOPO1, SRTM and ASTER GDEM have been published for geoscience applications. The latest product, ALOS DEM was announced to be available for a global coverage in 2016. This study examined the performance of ALOS-DEM in describing accurate morphometric and volumetric measurement of land features. A comparison was made on basis of DEM and DSM data of airborne full-waveform LiDAR data. Results showed that ALOS DEM is more approximately in reality an ALOS DSM which reveals the ground envelop surface rather than the ground bare surface. The differences between ALOS DEM and LiDAR DSM are mainly from 0 to 2.75 m with a standard deviation of 1.58 m. The differences between ALOS DEM and LiDAR DEM give a bias of as large as 20m, mostly located at the areas with abrupt change of relief and mainly in the north-facing slopes. This is probably due to ALOS sensor's geometry in corresponding to its looking-direction. The stream networks derived from both ALOS DEM and LiDAR DEM are in good agreement. It is suggested that further studies on methods for assessing geomorphometric changes in landform structures should be developed and compared.
{"title":"Accuracy evaluation of ALOS DEM with airborne LiDAR data in Southern Taiwan","authors":"Jin-King Liu, K. Chang, Chinsu Lin, Liang-Cheng Chang","doi":"10.1109/IGARSS.2015.7326453","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326453","url":null,"abstract":"Recently, some global-scale DEM products, such as GTOPO30, ETOPO1, SRTM and ASTER GDEM have been published for geoscience applications. The latest product, ALOS DEM was announced to be available for a global coverage in 2016. This study examined the performance of ALOS-DEM in describing accurate morphometric and volumetric measurement of land features. A comparison was made on basis of DEM and DSM data of airborne full-waveform LiDAR data. Results showed that ALOS DEM is more approximately in reality an ALOS DSM which reveals the ground envelop surface rather than the ground bare surface. The differences between ALOS DEM and LiDAR DSM are mainly from 0 to 2.75 m with a standard deviation of 1.58 m. The differences between ALOS DEM and LiDAR DEM give a bias of as large as 20m, mostly located at the areas with abrupt change of relief and mainly in the north-facing slopes. This is probably due to ALOS sensor's geometry in corresponding to its looking-direction. The stream networks derived from both ALOS DEM and LiDAR DEM are in good agreement. It is suggested that further studies on methods for assessing geomorphometric changes in landform structures should be developed and compared.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"441 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115202150","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326103
Lili Xu, Baolin Li, Yecheng Yuan, Zhang Tao
Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes of the terrestrial surface. Here, a temporal-spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated MODIS13Q1 pixels based on reliable MODIS13Q1 data. NDVIs of contaminated pixels were firstly computed through linear interpolation of adjacent high-quality pixels in the temporal series. Then, undetermined NDVIs of contaminated pixels were derived using the NDVI of the high-quality pixel that reflected the most similar land cover within the same ecological region, based on the weighted trajectory distance algorithm. These two steps were repeated iteratively, taking the estimated NDVIs as high-quality NDVIs to estimate other undetermined NDVIs of contaminated pixels until all NDVIs of contaminated pixels were estimated. The accuracies of estimated NDVIs using TSI were clearly higher than the asymmetric Gaussian, Savitzky-Golay, and window-regression methods; root mean square error and mean absolute percent error decreased by 14.0-104.8% and 19.4-47.3%, respectively. Furthermore, the TSI method performed better over a variety of environmental conditions. Variation of performance by the compared methods was 8.8-17.0 times than that of the TSI method. The TSI method will be most applicable when large amount of contaminated pixels exist.
{"title":"A novel method to reconstruct normalized difference vegetation index time series based on temporal-spatial iteration estimation","authors":"Lili Xu, Baolin Li, Yecheng Yuan, Zhang Tao","doi":"10.1109/IGARSS.2015.7326103","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326103","url":null,"abstract":"Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes of the terrestrial surface. Here, a temporal-spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated MODIS13Q1 pixels based on reliable MODIS13Q1 data. NDVIs of contaminated pixels were firstly computed through linear interpolation of adjacent high-quality pixels in the temporal series. Then, undetermined NDVIs of contaminated pixels were derived using the NDVI of the high-quality pixel that reflected the most similar land cover within the same ecological region, based on the weighted trajectory distance algorithm. These two steps were repeated iteratively, taking the estimated NDVIs as high-quality NDVIs to estimate other undetermined NDVIs of contaminated pixels until all NDVIs of contaminated pixels were estimated. The accuracies of estimated NDVIs using TSI were clearly higher than the asymmetric Gaussian, Savitzky-Golay, and window-regression methods; root mean square error and mean absolute percent error decreased by 14.0-104.8% and 19.4-47.3%, respectively. Furthermore, the TSI method performed better over a variety of environmental conditions. Variation of performance by the compared methods was 8.8-17.0 times than that of the TSI method. The TSI method will be most applicable when large amount of contaminated pixels exist.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116108534","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}
Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7326524
Hui Li, L. Jing, Yunwei Tang, Qingjie Liu, H. Ding, Zhongchang Sun, Yu Chen
Various multispectral (MS) and panchromatic (PAN) fusion (or pan-sharpening) algorithms were developed to produce an enhanced MS image of high spatial resolution. Regarding the novelty in both the PAN and MS bands of the WV-2 imagery, the objective of this study is to assess the performance of nine state-of-the-art pan-sharpening methods for the WV-2 imagery, using both image quality indices and information indices that used for urban information extraction. The comparison of the four quality indices (RASE, ERGAS, SAM, and Q4) demonstrated that the HR method performed the best for the WV-2 MS and PAN images. However, the comparison of the four information indices showed that a higher quality at data level does not signify better information preservation for object recognition.
{"title":"Assessment of pan-sharpening methods applied to WorldView-2 image fusion","authors":"Hui Li, L. Jing, Yunwei Tang, Qingjie Liu, H. Ding, Zhongchang Sun, Yu Chen","doi":"10.1109/IGARSS.2015.7326524","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326524","url":null,"abstract":"Various multispectral (MS) and panchromatic (PAN) fusion (or pan-sharpening) algorithms were developed to produce an enhanced MS image of high spatial resolution. Regarding the novelty in both the PAN and MS bands of the WV-2 imagery, the objective of this study is to assess the performance of nine state-of-the-art pan-sharpening methods for the WV-2 imagery, using both image quality indices and information indices that used for urban information extraction. The comparison of the four quality indices (RASE, ERGAS, SAM, and Q4) demonstrated that the HR method performed the best for the WV-2 MS and PAN images. However, the comparison of the four information indices showed that a higher quality at data level does not signify better information preservation for object recognition.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122288442","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：