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Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data

APPLIED GEOMATICS 2019年第3期11卷 277-288页

作者： Cass, Alexander Petropoulos, George P. Ferentinos, Konstantinos P. Pavlides, Andrew Srivastava, Prashant K. Aberystwyth Univ Dept Geog & Earth Sci Aberystwyth SY23 3DB Dyfed Wales Hellen Agr Org HAO Demeter Inst Ind & Forage Crops Dept Soil & Water Resources Larisa Greece Tech Univ Crete Sch Mineral Resources Engn Iraklion Greece Hellen Agr Org Demeter Inst Soil & Water Resources Dept Agr Engn Athens Greece Banaras Hindu Univ Inst Environm & Sustainable Dev Varanasi Uttar Pradesh India Banaras Hindu Univ DST Mahamana Ctr Excellence Climate Change Res Varanasi Uttar Pradesh India

Earth Observation (EO) provides a unique means of obtaining information on land use/cover and of its changes, which is of key importance in many scientific and practical applications. EO data is already widely used, for example, in environmental practices or decision-making related to food availability and security. As such, it is imperative to examine the suitability of different EO datasets, including their synergies, in respect to their ability to create products and tools for such practices and to guide effectively such decisions. This work aims at exploring the added value of the synergistic use of optical and radar data (from the Landsat TM and Advanced Synthetic Aperture Radar (ASAR) sensors respectively). Such information can help towards improving the accuracy of land cover classifications from EO datasets. As a case study, the region of Wales in the UK has been used. Two classifications-one based on optical data alone and another one developed from the synergy of optical and RADAR datasets acquired nearly, concurrently were developed for the studied region. Evaluation of the derived land/use cover maps was performed on the basis of the confusion matrix using validation points derived from a Phase 1 habitat map of Wales. The results showed 15% increase in overall accuracy (84% from 69%) and kappa coefficient (0.81 from 0.65) using the synergistic approach over the scenario where only optical data were used in the classification. In addition, McNemar's test was used to assess the statistical significance of the obtained results. Results of this test provided further confirmed that the use of optical data synergistically with the radar data provides more accurate land use/cover maps in comparison with the use of optical data alone.

关键词： Landsat ASAR SAR Land use cover mapping object-based classification classification Earth Observation GIS

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A novel method of aerial image classification based on attention-based local descriptors

A novel method of aerial image classification based on atten...

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International Conference on Mining Science and Technology

作者： Xu, Sheng Fang, Tao Huo, Hong Li, Deren Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China

This paper proposes a novel method for object-based classification in very high spatial resolution aerial image. It combines the saliency maps very closely to extract the conspicuous local regions for better description of object and classification. Unlike the previous work on detection of the local regions, a biologically motivated selective attention model is presented in this paper, since not all the local regions are important for describing the objects. In order to model the attention region, we propose a new attention-based local descriptor using the saliency map and relative local features to reflect the region of interest (ROI). The experimental results on the VHR aerial image dataset show that the proposed approach can obtain the state-of-the-art classification performance.

关键词： Bag-of-visual-words object-based classification saliency map

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Evaluating the Potential of GF-1 Pan-multispectral Camera Imagery for Identifying the Quasi-circular Vegetation Patches in the Yellow River Delta, China 11

Evaluating the Potential of GF-1 Pan-multispectral Camera Im...

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11th International Conference on Digital Image Processing (ICDIP)

作者： Liu, Qingsheng Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Resources & Environm Informat Syst 11A Datun Rd Beijing 100101 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Gaofen-1 (GF-1) is the first high resolution optical imaging satellite of China primary civilian Earth observation project under the China High- resolution Earth Observation System. With the GF-1 trio launched on March 31, 2018, the combined 3+1 GF-1 constellation will offer one-day revisit coverage, which provides more available high resolution remote sensing images for monitoring decametric-scale vegetation patches and long-term pattern dynamics. In this study, a panchromatic image, the original multispectral image, and the pan-sharpened multispectral image from GF-1 Pan-multispectral Camera was compared to evaluate their potential for identifying the quasi-circular vegetation patches (QVPs) in the Yellow River Delta, China. The results showed that high spatial resolution was more important for identifying the QVPs than more additional spectral bands of GF-1, and the pan-sharpened multispectral image had the highest identification accuracy of the QVPs (F-measure is 64.6%). In the future, multi-temporal pan-sharpened GF-1 images should be used to obtain higher accuracy, which is basis for studying vegetation patch pattern dynamics.

关键词： GF-1 pan-multispectral camera imagery object-based classification support vector machine quasi-circular vegetation patch Yellow River Delta

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Interpretability of TerraSAR-X fused data

Interpretability of TerraSAR-X fused data

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SPIE Conference on Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX

作者： Rosso, Pablo H. Ehlers, Manfred Klonus, Sascha Univ Osnabruck Inst Geoinformat & Remote Sensing D-49069 Osnabruck Germany

ISBN: (纸本)9780819477835

Pansharpening is an image fusion technique that combines the spatial structure of a high resolution panchromatic image with the spectral information of a lower resolution multispectral image to produce a high resolution multispectral image. Image data of the new German RADAR satellite TerraSAR-X were used to sharpen optical multispectral data. To assess the advantages and limitations of fusion, the interpretability of terrain features at different image resolutions was determined. We concluded that a resolution ratio of 1:10 (TerraSAR: Multispectral) is optimal to benefit from the synergism of a SAR/multispectral fusion. Q index and an object-based classification were used to assess fusion quality and to compare their efficacy to determine the best fusion algorithms. Both approaches are appropriate methods to asses quality but only on judging some aspects of the fusion products. We conclude that a more comprehensive fusion quality assessment method still needs to be developed.

关键词： Pansharpening image fusion TerraSAR-X Q index object-based classification

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object-based DETECTION OF HAZELNUT ORCHARDS USING VERY HIGH RESOLUTION AERIAL PHOTOGRAPHS 12

OBJECT-BASED DETECTION OF HAZELNUT ORCHARDS USING VERY HIGH ...

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12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Tumer, Ilay Nur Sengul, Gafur Semi Sertel, Elif Ustaoglu, Beyza Istanbul Tech Univ ITU Dept Geomat Engn Fac Civil Engn TR-34469 Istanbul Turkiye Sakarya Univ Dept Geog Fac Human & Social Sci TR-54050 Sakarya Turkiye

ISBN: (纸本)9798350380613;9798350380606

Hazelnuts are a vital agricultural commodity, contributing significantly to global food systems and public health due to their nutritional value and economic importance as an export crop. Turkiye is a leading global producer of hazelnuts as a major source of income and a strategic agricultural product. In this study, we selected two regions named Acmabasi and Parali from Sakarya province which ranks third in the production of hazelnut among Turkish provinces and utilized very high-resolution (VHR) aerial photographs to classify hazelnut fields. In addition to the standard CORINE Land Cover (LC) classes, we defined a specific hazelnut class, verified through field observations. Various machine learning-based classification algorithms, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Bayesian classification, were employed with object-based classification with different feature values. The performance of the models was evaluated using overall accuracy and F1-score metrics and the best results are obtained with Support Vector Machines (SVM) with Radial Basis Function (rbf) and Bayes classifier. We obtain 96.20% overall accuracy and 93.40% F1-Score for Acmabasi while using Bayes with feature combination as a best result. For Parali region, the highest overall accuracy is obtained with SVM - rbf using feature combination while F1-score is the highest for Bayes classifier with 90.57%.

关键词： hazelnut remote sensing very-high resolution imagery object-based classification

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Conceptual Framework of Combined Pixel and object-based Method for Delineation of Debris-Covered Glaciers

Conceptual Framework of Combined Pixel and Object-based Meth...

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ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People

作者： Sahu, R. Gupta, R. D. Motilal Nehru Natl Inst Technol GIS Cell Allahabad Uttar Pradesh India Motilal Nehru Natl Inst Technol Dept Civil Engn Allahabad Uttar Pradesh India

Delineation of the glacier is an important task for understanding response of glaciers to climate. In Himalayan region, most of the glaciers are covered with debris. Supraglacial debris works as an obstacle for automatic mapping of glacier using remote sensing data. Different methods have been used to reduce this difficulty based on pixel-based and object-based approaches using optical data, thermal data and DEM. Pixel-based glacier mapping is a traditional method for delineation of the glacier but the object-based method has emerged as a new approach in cryosphere application leading to its successful application in different applications. All pixel-based methods require some degree of manual correction because these can't be delineated automatically, especially in shadow area and debris covered part of the glacier. In the majority of studies, the object-based method has provided higher accuracy to delineate the debris-covered glacier. Spatially high spatial resolution satellite data is best suited for object-based image classification. In future, a combination of pixel-based method and object-based method can be attempted for delineation of the debris-covered glacier along with its critical analysis for suitability. The present paper critically reviews pixel-based and object-based methods as well as provides a framework for combined pixel and object-based method for delineation of debris-covered glacier.

关键词： Pixel-based classification object-based classification Debris-covered glacier Conceptual framework

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AUTOMATIC MULTI-SCALE SEGMENTATION OF HIGH SPATIAL RESOLUTION SATELLITE IMAGES USING WATERSHEDS

AUTOMATIC MULTI-SCALE SEGMENTATION OF HIGH SPATIAL RESOLUTIO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Sahin, Kerem Ulusoy, Ilkay METU Dept Elect & Elect Engn METUVIS Lab Ankara Turkey

ISBN: (纸本)9781479911141

An automatic segmentation algorithm that is based on watersheds and region merging type multi-scale segmentation (MSS) is proposed, which can be used as the initial step of an object-based classifier. First, the image is segmented using watershed segmentation. Then, primitive segments are merged to create meaningful objects by the proposed hybrid region merging algorithm. During these steps, an unsupervised segmentation accuracy metric is considered so that best performing parameters of the proposed algorithm are determined automatically. By this way, the proposed segmentation algorithm has become fully automatic. Experiments are done on images taken from Google Earth (R) software. Algorithm performance is computed using the ground truths of these images. Also, results for some other Google Earth (R) images are presented for qualitative performance assessment.

关键词： Watershed segmentation multi-scale segmentation automatic segmentation high spatial resolution satellite images object-based classification

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FUSION OF OPTICAL IMAGERY AND SYNTHETIC APERTURE RADAR (SAR) FOR ECOLOGICAL CHANGE DETECTION OF NIPA PALM IN THAILAND 39

FUSION OF OPTICAL IMAGERY AND SYNTHETIC APERTURE RADAR (SAR)...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Bencure, Jannet C. Tripathi, Nitin Kumar Asian Inst Technol Remote Sensing & Geog Informat Syst Khlong Nueng Thailand Visayas State Univ Dept Geodet Engn Baybay City Philippines

ISBN: (纸本)9781538691540

Assessing the causes of nipa palm reduction is significant for the local government in tambon Khanap Nak whose people's livelihood mainly depends on it. The objectives of the study were to assess quantitatively the change in the distribution of Nipa palm for 16 years, from 1993 to 2009. Optical imagery and SAR (synthetic aperture radar) from JERS-1 and ALOS were synergized to accordingly extract nipa palm. The study findings results show that there was a rapid reduction in the Nipa palm area during 1993-2006, while gradual reduction rate observed in period 2006-2009. Factors that caused the reduction were identified.

关键词： Nipa palm change detection object-based classification JERS-1 ALOS

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Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain)

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APPLIED GEOMATICS 2012年第4期4卷 245-255页

作者： Hermosilla, T. Diaz-Manso, J. M. Ruiz, L. A. Recio, J. A. Fernandez-Sarria, A. Ferradans-Nogueira, P. Univ Politecn Valencia GeoEnvironm Cartog & Remote Sensing Res Grp Camino Vera S-N E-46022 Valencia Spain Sociedade Desenvolvemento Comarcal Galicia Santiago De Compostela Spain

The abandonment of agricultural plots entails a low economic productivity of the land and a higher vulnerability to wildfires and degradation of affected areas. In this sense, the local government of Galicia is promoting new methodologies based on high-resolution images in order to classify the territory in basic and generic land uses. This procedure will be used to control the sustainable management of plots belonging to the Land Bank. This paper presents an application study for maintaining and updating land use/land cover geospatial databases using parcel-oriented classification. The test is performed over two geographic areas of Galicia, in the northwest of Spain. In this region, forest and shrublands in mountain environments are very heterogeneous with many private unproductive plots, some of which are in a high state of abandonment. The dataset is made of high spatial resolution multispectral imagery, cadastral cartography employed to define the image objects (plots), and field samples used to define evaluation and training samples. A set of descriptive features is computed quantifying different properties of the objects, i.e. spectral, texture, structural, and geometrical. Additionally, the effect on the classification and updating processes of the historical land use as a descriptive feature is tested. Three different classification methodologies are analyzed: linear discriminant analysis, decision trees, and support vector machine. The overall accuracies of the classifications obtained are always above 90 % and support vector machine method is proved to provide the best performance. Forest and shrublands areas are especially undefined, so the discrimination between these two classes is low. The results enable to conclude that the use of automatic parcel-oriented classification techniques for updating tasks of land use/land cover geospatial databases, is effective in the areas tested, particularly when broad and well defined classes are required.

关键词： object-based classification Change detection High-resolution imagery Mapping Agriculture

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The applicability of FORMOSAT-2 images to coastal waters/bodies classification 17

The applicability of FORMOSAT-2 images to coastal waters/bod...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII part of the International Symposium on Remote Sensing

作者： Teodoro, Ana Duarte, Lia Silva, Pedro Univ Porto Fac Sci Earth Sci Inst ICT P-4169007 Oporto Porto Portugal Univ Porto Fac Sci Dept Geosci Environm & Land Planning P-4169007 Oporto Portugal

ISBN: (纸本)9781628418477

FORMOSAT-2, launched in May 2004, is a Taiwanese satellite developed by the National Space Organization (NSPO) of Taiwan. The Remote Sensing Instrument (RSI) is a high spatial-resolution optical sensor onboard FORMOSAT-2 with a 2 m spatial resolution in the panchromatic (PAN) band and a 8 m spatial resolution in four multispectral (MS) bands from the visible to near-infrared region. The RSI images acquired during the daytime can be used for land cover/use studies, natural and forestry resources, disaster prevention and rescue works. The main objectives of this work were to investigate the application of FORMOSAT-2 data in order to: (1) identify beach patterns;(2) correctly extract a sand spit boundary. Different pixel-based and object-based classification algorithms were applied to four FORMOSAT-2 scenes and the results were compared with the results already obtained in previous works. Analyzing the results obtained, is possible to conclude that the FORMOSAT-2 data are adequate to the correct identification of beach patterns and to an accurately extraction of the sand spit boundary (Douro river estuary, Porto, Portugal). The results obtained were compared with the results already achieved with IKONOS-2 images. In conclusion, this research has demonstrated that the FORMOSAT-2 data and image processing techniques employed are an effective methodology to identify beach patterns and to correctly extract sand spit boundaries. In the future more FORMOSAT-2 images will be processed and will be consider the use of pan sharped images and data mining algorithms.

关键词： Beach patterns sand spit pixel-based classification object-based classification Spring software

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