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Pixel-versus object-based classification of forest and agricultural areas from multiresolution satellite images

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第1期25卷 365-375页

作者： Boyaci, Dijle Erdogan, Mustafa Yildiz, Ferruh Gen Command Mapping Dept Photogrammetry Ankara Turkey Selcuk Univ Fac Engn Dept Geomat Engn Konya Turkey

Managing of natural resources including agriculture and forestry is a very important subject for governments and decision makers. Up-to-date, accurate, and timely geospatial information about natural resources is needed in the management process. Remote sensing technology plays a significant role in the production of this geospatial information. Compared to terrestrial work, the analysis of larger areas with remote sensing techniques can be done on a shorter timescale and at lower cost. Image classification in remote sensing is one of the most popular methods used for the detection of forest and agricultural areas. However, the accuracy of classification changes according to the source and reference data, the classification method, and the producer's knowledge and experience. In this research, the identification of forests and agricultural areas was studied in terms of both their geometry and attribution using different classification methods and different source data. Landsat, Aster, and RapidEye images, which have different spatial and spectral resolution, were used as the source data. Pixel- and object-based classification algorithms were also tested. classification accuracy results were evaluated at 300 stratified random pixels. It was found that the best overall accuracy was obtained from Aster imagery with object-based classification using the nearest neighbor method. The results also showed that spatial resolution is important for discrimination of classes and spectral resolution is important for definition of features, and confirmed the well-established paradigm of remote sensing that there are no perfect source data or method of classification for all situations.

关键词： Indices object-based classification pixel-based classification spatial resolution spectral resolution segmentation

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Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery

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COMPUTERS & GEOSCIENCES 2012年 41卷 99-107页

作者： Petropoulos, George P. Kalaitzidis, Chariton Vadrevu, Krishna Prasad Univ Bristol Dept Earth Sci Bristol BS8 1RJ Avon England Fdn Res & Technol Hellas FORTH Inst Appl & Computat Math GR-70013 Iraklion Greece Mediterranean Agron Inst Chania GR-73100 Iraklion Greece Univ Maryland Dept Geog College Pk MD 20742 USA

The Hyperion hyperspectral sensor has the highest spectral resolution, acquiring spectral information of Earth's surface objects in 242 spectral bands at a spatial resolution of 30 m. In this study, we evaluate the performance of the Hyperion sensor in conjunction with the two different classification algorithms for delineating land use/cover in a typical Mediterranean setting. The algorithms include pixel-based support vector machines (SVMs) and the object-based classification algorithm. Validation of the derived land-use/cover maps from the above two algorithms was performed through error matrix statistics using the validation points from the very high resolution QuickBird imagery. Results suggested both classifiers as highly useful in mapping land use/cover in the study region with the object-based approach slightly outperforming the SVMs classification by overall higher classification accuracy and Kappa statistics. Results from the statistical significance testing using McNemar's chi-square test confirmed the superiority of the object-oriented approach compared to SVM. The relative strengths and weaknesses of the two classification algorithms for land-use/cover mapping studies are highlighted. Overall, our results underline the potential of hyperspectral remote sensing data together with an object-based classification approach for mapping land use/cover in the Mediterranean regions. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Land-cover/use mapping Hyperion object-based classification Support Vector machines Remote sensing

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Cropland Extraction from Very High Spatial Resolution Satellite Imagery by object-based classification Using Improved Mean Shift and One-Class Support Vector Machines

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SENSOR LETTERS 2011年第3期9卷 997-1005页

作者： Shen, Jing Liu, Jiping Lin, Xiangguo Zhao, Rong Xu, Shenghua Chinese Acad Surveying & Mapping Res Ctr Govt Geog Informat Syst Beijing 100830 Peoples R China Chinese Acad Surveying & Mapping Key Lab Mapping Space State Bur Surveying & Mapping Beijing 100830 Peoples R China

The issue of cropland extraction from very high spatial resolution (VHR) satellite imagery remains a great challenge. In this paper, an object-based classification method for cropland extraction from VHR satellite imagery is proposed based on the improved mean shift and one-class SVM. After the fused satellite image is transformed by nonnegative matrix factorization into three bands, the improved mean shift is employed to segment the image. Subsequently, the structure lines of each region in the segmented image are detected, and the standard deviations of the directions of the straight lines are calculated. The spectral information and the above derived texture information are selected as features for the following classification. At last, the support vector data description is utilized to recognize the croplands from the segmented image based on only some cropland samples. Three satellite images with different spatial resolutions are employed to test the algorithm, and the results show that our proposed method obtains a higher overall classification accuracy than the eCognition's method does, and its overall classification accuracy is promoted with the increasing of spatial resolution. Another merit of our method is that it needs only the cropland samples, which is time-saving and cost-saving.

关键词： Cropland Extraction Very High Spatial Resolution object-based classification Mean Shift SVM

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Perturbed peer model with joint confidence for semi-supervised object-based classification in urban area

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第14期44卷 4417-4440页

作者： Wang, Wenye Zhang, Xueliang Xiao, Pengfeng Su, Qi Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing Applicat Min Nanjing Jiangsu Peoples R China Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing Applicat Min Nanjing 210023 Jiangsu Peoples R China

The application of deep learning (DL) improves the accuracy of object-based classification in urban area, but the huge numbers of labelled samples required for training DL models are difficult to obtain. To address that, we propose a novel semi-supervised object-based classification method that combines pseudo-labelling method and consistency regularization method, named as perturbed peer model (PPM). To evaluate the quality of unlabelled object samples, a new object-level joint confidence is designed to assess the confidence of the samples' prediction, which provides guidance for selecting unlabelled object samples in semi-supervised object-based classification. Instead of only using the high-confidence samples as usual, both the high-confidence samples with discriminative power and the low-confidence samples with a large range of structural features are exploited using pseudo-labelling method and consistency regularization method, respectively, facilitating the fusion of the two methods in the proposed PPM. In addition, two types of perturbations, dropout and difference augmentation, are integrated into the model to drive the consistency regularization method as well as to enhance the difference to facilitate the pseudo-labelling method. Experimental results show that the classification accuracy of PPM is better than the widely-used self-training, co-training, noisy-student, unsupervised data augmentation for consistency training, and FreeMatch methods in urban area.

关键词： object-based classification semi-supervised learning convolutional neural networks high-spatial resolution remote sensing urban area

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Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region

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GEOCARTO INTERNATIONAL 2013年第2期28卷 114-129页

作者： Petropoulos, George P. Vadrevu, Krishna Prasad Kalaitzidis, Chariton Aberystwyth Univ Inst Geog & Earth Sci Aberystwyth SY23 2EJ Dyfed Wales Univ Maryland Dept Geog Sci Boston MA USA Mediterranean Agron Inst Chania Khania Crete Greece

In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting.

关键词： Hyperion spectral angle mapper object-based classification hyperspectral land cover/use mapping

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Enhanced snow cover mapping using object-based classification and normalized difference snow index (NDSI)

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EARTH SCIENCE INFORMATICS 2023年第3期16卷 2813页

作者： Raghubanshi, Sudhanshu Agrawal, Ritesh Rathore, Bhanu Prakash Space Applicat Ctr ISRO Ahmadabad 380015 Gujarat India

The study aims to improve the classification and mapping of snow cover over the Himalayan region, which is essential for assessing water availability and understanding hydrological and climatic interactions. The normalized difference snow index (NDSI) is a traditional digital classification method for snow cover mapping. However, it is not always effective in differentiating snow from other features such as water bodies and shadows of mountain hills. In this study, an improved methodology for snow cover mapping was developed using an object-based classification with NDSI and normalized difference water index (NDWI) over segmented objects instead of pixels to separate snow and water with reduced noise. Shepherd segmentation was used to generate spatially homogeneous objects associated with ground cover features. The study focused on the Chandra basin in Himachal Pradesh, India, using an Indian Remote Sensing Satellite (IRS-P6) LISS-III optical image from 30-09-2016. The developed framework was tested using an object-based NDSI classification and further improved with an object-based NDSI-NDWI classification and validated against a manually digitized snow cover map. Validation showed that the object-based NDSI-NDWI classification provided a significant improvement in snow cover mapping compared to traditional NDSI classification, reducing the overestimation of snow-covered areas by up to 6.14%. The developed methodology was executed in the Python environment with efficient computing power. This study demonstrates that an integrated analysis of object-based classification with NDSI and NDWI, can significantly improve snow cover mapping by separating non-snow features. The results of this study show that they have the potential to be extended to larger regions with snow cover.

关键词： Snow cover NDSI NDWI object-based classification LISS-III

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Supervised and Adaptive Feature Weighting for object-based classification on Satellite Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2018年第9期11卷 3224-3234页

作者： Zhou, Ya'nan Chen, Yuehong Feng, Li Zhang, Xin Shen, Zhanfeng Zhou, Xiaocheng Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Jiangsu Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100101 Peoples R China Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350116 Fujian Peoples R China

object-based image analysis (OBIA) technique has been representing an evolving paradigm of remote sensing application, along with more high-resolution satellite images available. However, too many derived features from segmented objects also present a new challenge to OBIA applications. In this paper, we present a supervised and adaptive method for ranking and weighting features for object-based classification. The core of this method is the feature weight maps for each land type resulted from prior thematic maps and their corresponding satellite images of study areas. Specifically, first, satellite images to he classified are segmented using an adaptive multiscale algorithm, and the multiple (spectral, shape, and texture) features of segmented objects are calculated. Second, we extract distance maps and feature weight vectors for each land type from the prior thematic maps and corresponding satellite images, to generate feature weight maps. Third, a feature-weighted classifier with the feature weight maps, is applied on the segmented objects to generate classification maps. Finally, the classification result is evaluated. This approach is applied on a Sentinel-2 multispectral satellite image and a Google Map image to produce objected-based classification maps, compared with the traditional feature selection algorithms. The experimental results illustrate that the proposed method is practically efficient to select important features and improve classification performance.

关键词： Distance map feature weight maps object-based classification prior thematic map

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A multiple-point spatially weighted k-NN method for object-based classification

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年第0期52卷 263-274页

作者： Tang, Yunwei Jing, Linhai Li, Hui Atkinson, Peter M. Chinese Acad Sci Inst Remote Sensing & Digital Earth Key Lab Digital Earth Sci 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ Lancaster Fac Sci & Technol Engn Bldg Lancaster LA1 4YR England Queens Univ Belfast Sch Geog Archaeol & Palaeoecol Belfast BT7 1NN Antrim North Ireland Univ Southampton Geog & Environm Southampton SO17 1BJ Hants England

object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Multiple-point statistics k-NN object-based classification Training image

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Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 50卷 187-197页

作者： Pham, Lien T. H. Brabyn, Lars Ashraf, Salman Univ Waikato Dept Geog Tourism & Environm Planning Private Bag 3105 Hamilton 3240 New Zealand Univ Sci Vietnam Natl Univ Ho Chi Minh City Fac Environm Sci 227 Nguyen Van Cu StrDist 5 Hcmc Vietnam GNS Sci POB 30368 Lower Hutt 5040 New Zealand

There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects;Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings;geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees. (C) 2016 Elsevier B.V. All rights reserved.

关键词： object-based classification Pohutukawa Random forest Support vector machine QuickBird LiDAR

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Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 51卷 60-73页

作者： Rougier, Simon Puissant, Anne Stump, Andre Lachiche, Nicolas Univ Strasbourg CNRS UMR 7362 Lab ImageVilleEnvironmLIVE Strasbourg France Univ Strasbourg CNRS UMR 7516 IPGS Strasbourg France Univ Strasbourg UMR 7357 ICUBE Lab Sci Ingn Informat & Imagerie Strasbourg France

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Urban vegetation mapping object-based classification VHR satellite images Sampling strategies evaluation Active learning Random forest

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