Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis

被引:4
|
作者
Gianinetto, M. [1 ]
Rusmini, M. [1 ]
Marchesi, A. [1 ]
Maianti, P. [1 ]
Frassy, F. [1 ]
Dalla Via, G. [1 ]
Dini, L. [2 ]
Nodari, F. Rota [1 ]
机构
[1] Politecn Milan, Lab Remote Sensing, Dept Architecture Built Environm & Construct Engn, I-20133 Milan, Italy
[2] Italian Space Agcy ASI, Space Geodesy Ctr, I-75100 Matera, Italy
关键词
COSMO-SkyMed (CSK (R)); data integration; GeoEye-1 (GE1); object-based image analysis (OBIA); thematic classification; HIGH-RESOLUTION SAR; LAND-COVER; ORIENTED CLASSIFICATION; AREAS; MULTISENSOR; LIDAR;
D O I
10.1109/JSTARS.2015.2425211
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper describes the potentialities of data integration of high spatial resolution multispectral (MS) and single-polarization X-band radar for object-based image analysis (OBIA) using already available algorithms and techniques. GeoEye-1 (GE1) MS images (0.5/2.0 m) and COSMO-SkyMed (CSK (R)) stripmap images (3.0 m) were collected over a complex test site in the Venetian Lagoon, made up of an intricate mixture of settlements, cultivations, channels, roads, and marshes. The validation confirmed that the integration of optical and radar data substantially increased the thematic accuracy [about 20%-30% for overall accuracy (OA) and about 25%-35% for k coefficient] of MS data, and unlike the outcomes of some new researches, also confirmed that, with appropriate preprocessing, traditional OBIA could also be applied to X-band radar data without the need of developing ad hoc algorithms.
引用
收藏
页码:2282 / 2293
页数:12
相关论文
共 50 条
  • [1] GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments
    Aguilar, M. A.
    Saldana, M. M.
    Aguilar, F. J.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (07) : 2583 - 2606
  • [2] ANALYSIS OF THE POTENTIALITY OF MULTI-TEMPORAL COSMO-SKYMED® DATA FOR CLASSIFYING SUMMER CROPS
    Guarini, Rocchina
    Bruzzone, Lorenzo
    Santoni, Massimo
    Vuolo, Francesco
    Dini, Luigi
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 3170 - 3173
  • [3] Detection of forest windthrows with bitemporal COSMO-SkyMed and Sentinel-1 SAR data
    Dalponte, Michele
    Solano-Correa, Yady Tatiana
    Marinelli, Daniele
    Liu, Sicong
    Yokoya, Naoto
    Gianelle, Damiano
    REMOTE SENSING OF ENVIRONMENT, 2023, 297
  • [4] Persistent point scatterer statistical analysis for X-band SAR data: the Cosmo-SkyMed case study
    Guccione, P.
    Zonno, M.
    Mascolo, L.
    D'Introno, S.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2014, 35 (01) : 127 - 148
  • [5] The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data
    Xu, Dawei
    Chen, Baorui
    Shen, Beibei
    Wang, Xu
    Yan, Yuchun
    Xu, Lijun
    Xin, Xiaoping
    RANGELAND ECOLOGY & MANAGEMENT, 2019, 72 (02) : 318 - 326
  • [6] Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images
    Hamedianfar, Alireza
    Shafri, Helmi Zulhaidi Mohd
    Mansor, Shattri
    Ahmad, Noordin
    JOURNAL OF APPLIED REMOTE SENSING, 2014, 8
  • [7] Object-Based Image Analysis in Wetland Research: A Review
    Dronova, Iryna
    REMOTE SENSING, 2015, 7 (05) : 6380 - 6413
  • [8] Forest Mapping Through Object-based Image Analysis of Multispectral and LiDAR Aerial Data
    Machala, Martin
    Zejdova, Lucie
    EUROPEAN JOURNAL OF REMOTE SENSING, 2014, 47 : 117 - 131
  • [9] Object-based image analysis supported by data mining to discriminate large areas of soybean
    da Silva Junior, Carlos Antonio
    Nanni, Marcos Rafael
    de Oliveira-Junior, Jose Francisco
    Cezar, Everson
    Teodoro, Paulo Eduardo
    Delgado, Rafael Coll
    Shiratsuchi, Luciano Shozo
    Shakir, Muhammad
    Chicati, Marcelo Luiz
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2019, 12 (03) : 270 - 292
  • [10] Integration of high-resolution imagery and LiDAR data for object-based classification of urban area
    Mehta, A.
    Dikshit, O.
    Venkataramani, K.
    GEOCARTO INTERNATIONAL, 2014, 29 (04) : 418 - 432