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Correction of Terrain Effects on Forest Canopy Height Estimation Using ICESat-2 and High Spatial resolution Images

REMOTE SENSING 2022年第18期14卷 4453-4453页

作者： Li, Bin Zhao, Tianzhong Su, Xiaohui Fan, Guangpeng Zhang, Wenjie Deng, Zhuo Yu, Yonghui Beijing Forestry Univ Sch Informat Sci & Technol Beijing 100083 Peoples R China Natl Forestry & Grassland Adm Engn Res Ctr ForestryOriented Intelligent Informa Beijing 100083 Peoples R China Beijing Forestry Univ Forestry Informat Res Inst Beijing 100083 Peoples R China Guangxi Gaofeng Forest Farm Nanning 530001 Peoples R China

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), enabling global canopy height measurements from forest canopy height models (CHMs). Topographic slope is a crucial factor affecting the accuracy of canopy height estimates from ICESat-2 CHMs, but it has not been sufficiently studied. This paper aims to eliminate the influence of slope on canopy height estimates from ICESat-2 data and establishes a method for correcting forest canopy heights based on high spatial resolution digital orthophoto maps (DOM). The cross-track photons are corrected horizontally to eliminate the estimation error. multi-resolution segmentation is used to segment tree crowns in the DOM, and the distance and relative position between the top of canopy (TOC) photons and the center point of the crown are calculated. TOC photon correction rules are established for different terrains, and the vertical error of the TOC photons is corrected. The results indicate that the vertical error increases exponentially with the slope. The cross-track photon correction and the TOC photon correction method eliminate the effect of slope on canopy height estimates. The cross-track photon correction method reduces the mean absolute error (MAE) and root mean square error (RMSE) of the canopy height estimates by 35.71% and 35.98%, respectively. The TOC photon correction approach further reduces the MAE and RMSE by 23% and 19.23%, respectively. The proposed method has significantly higher accuracy for forest canopy height estimation using ICESat-2 data than the traditional method.

关键词： ICESat-2 ATLAS canopy height model slope multi-resolution segmentation correction

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Land Cover/Land Use Mapping of LISS IV Imagery Using Object-Based Convolutional Neural Network with Deep Features

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2020年第1期48卷 145-154页

作者： Rajesh, S. Nisia, T. Gladima Arivazhagan, S. Abisekaraj, R. Mepco Schlenk Engn Coll Dept Informat Technol Sivakasi 626005 India AAA Coll Engn & Technol Dept Comp Sci & Engn Sivakasi 626005 India Mepco Schlenk Engn Coll Dept ECE Sivakasi 626005 India DRDO Naval Sci & Technol Lab Vishakapatanam 530627 India

The paper proposes a new method for classifying the LISS IV satellite images using deep learning method. Deep learning method is to automatically extract many features without any human intervention. The classification accuracy through deep learning is still improved by including object-based segmentation. The object-based deep feature learning method using CNN is used to accurately classify the remotely sensed images. The method is designed with the technique of extracting the deep features and using it for object-based classification. The proposed system extracts deep features using pre-defined filter values, thus increasing the overall performance of the process compared to randomly initialized filter values. The object-based classification method can preserve edge information in complex satellite images. To improve the classification accuracy and to reduce complexity, object-based deep learning technique is used. The proposed object-based deep learning approach is used to drastically increase the classification accuracy. Here, the remotely sensed images were used to classify the urban areas of Ahmadabad and Madurai cities. Experimental results show a better performance with the object-based classification.

关键词： Convolutional neural network (CNN) Deep learning LISS IV image Image classification multi-resolution segmentation

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Object-based land surface segmentation scale optimisation: An ill-structured problem

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GEOMORPHOLOGY 2019年 327卷 377-384页

作者： Louw, Gerrit van Niekerk, Adriaan Stellenbosch Univ Dept Geog & Environm Studies Private Bag 11 ZA-7602 Matieland South Africa

The segmentation of continuous land surfaces into morphologically representative objects has received increasing attention in recent years. multi-resolution segmentation (MRS) has been shown to delimit morphological boundaries accurately and the use of unsupervised data-driven local variance (LV) based methods for detecting characteristic levels of scale parameter (SP) in land surfaces has been established. However, whether the detected SPs accurately delimit target morphological features is unclear. This study illustrates that multi-resolution land surface segmentation SP optimisation is an ill-structured problem that can be divided into subsets of well-structured problems by defining "conceptual" morphometric primitive (henceforth referred to as morphometric primitive) conditions. A new methodology is proposed where an ensemble of unsupervised data-driven LV-based SP optimisation techniques are implemented to evaluate objects against each of the morphometric primitive conditions. To construct an ensemble of SP optimisation techniques, an established method, estimation of scale parameter 2 (ESP 2), is reviewed and existing LV concepts expanded to include two new SP optimisation techniques, namely: object boundary local variance (OBLV) and local variance ratio (LVR). Agreement between the different SP optimisation techniques are indicative of SPs where the probability that morphometric primitives are delimited is higher than when applying SPs selected by single SP optimisation approaches. (C) 2018 Published by Elsevier B.V.

关键词： Land surface segmentation multi-resolution segmentation Scale parameter optimisation Object-based geomorphometry

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High-resolution mapping of ash (Fraxinus spp.) in bottomland hardwoods to slow Emerald Ash Borer infestation

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SCIENCE OF REMOTE SENSING 2020年 1卷

作者： Sapkota, Bishwa B. Liang, Lu Univ Arkansas Monticello Sch Forest & Nat Resources Monticello AR 71655 USA Texas A&M Univ Dept Soil & Crop Sci 370 Olsen Blvd College Stn TX 77843 USA Univ North Texas Dept Geog & Environm 1155 Union Circle Denton TX 76203 USA

Ash (Fraxinus spp.) species are under serious threat by an invasive insect named the Emerald Ash Borer (EAB), already spreading across 35 states in the USA. To track the dramatic progression for effective implementation of ash conservation plans, information on the spatial distribution of host resources is essential. This study aimed at delineating ash trees at the crown level in a mature bottomland hardwood forest under a remote sensing mapping framework. We employed five-band Worldview-3 imagery at 31 cm spatial resolution acquired in the spring and conducted the mapping activity using geographic object-based image analysis (GEOBIA). Because the training samples and features play important roles in classification, we tested the effects of training sample size and the textural features on the mapping performance. A set of segments that were optimized through a series of test-andtrials in the multiresolution segmentation were used as the basic mapping unit. By testing against field samples, the largest average overall accuracy 82%, was achieved through the random forest classifier. Our findings suggest that the classification accuracy is improved with increased training sample size. The combination of spectral and textural features improved the classification accuracy significantly (p-value <0.05) for two large sample sizes (i.e. n = 200 and n = 230). The GEOBIA framework we demonstrated may guide the future ash tree mapping endeavors regarding the selection of segmentation parameters, sample sizes, and classification features.

关键词： Satellite imagery Random forest Textural features multi-resolution segmentation Remote sensing

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A multistep approach to classify full canopy and leafless trees in bottomland hardwoods using very high-resolution imagery

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JOURNAL OF SUSTAINABLE FORESTRY 2018年第4期37卷 339-356页

作者： Sapkota, Bishwa B. Liang, Lu Univ Arkansas Sch Forestry & Nat Resources POB 3468 Monticello AR 71656 USA Univ Arkansas Arkansas Forest Resources Ctr Div Agr Monticello AR USA

The distribution of leafless and full-canopy trees in fall is essential in understanding the phenological and morphological characteristics of forests and those are critical baseline information for several scientific questions and management scenarios. The rapid progress of very high-resolution satellite-borne sensors provides a feasible solution to fast and accurately map tree coverage during the growing season. However, less attention has been paid to the leaf-off seasons. One biggest challenge with classification on fall imagery is the potential confusion among different land features, as evidenced by the no significant difference between bare land and leafless trees from t-test and transformed divergence (TD) analysis results. We proposed an integrative approach to classifying leafless and full-canopy trees on fall imagery of 0.46-m WorldView-2 (WV-2). We adopted a two-step classification approach by first removing the non-forest area from the WV-2 imagery with the mask layer generated from the publicly available summer imagery of 1-m National Agriculture Imagery Program imagery via object-based image analysis. Two classification methods, namely decision tree (DT) and nearest neighborhood (NN), were employed to classify full canopy and leafless trees in the masked WV-2 imagery. DT performed slightly better than NN regarding higher overall accuracy and fewer feature requirements.

关键词： Remote sensing object-based classification multi-resolution segmentation ash trees phenology

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Classification of Siachen Glacier Using Object-Based Image Analysis 2

Classification of Siachen Glacier Using Object-Based Image A...

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2nd International Conference on Intelligent Circuits and Systems (ICICS)

作者： Sharda, Shikha Srivastava, Mohit IK Gujral Punjab Tech Univ Kapurthala India Chandigarh Engn Coll Mohali India

ISBN: (纸本)9781538664834

With the advancement in computer technologies, many algorithms have been developed that mimic the human behavior of accurately identifying the objects based on patterns, colors, textures, sizes, shapes. Based on the previous research, Object-Based Image Analysis (OBIA) approach has come out to be an emerging technology that can easily extract the objects like roads, buildings, rivers, lakes etc. from the remotely sensed image. In this paper, mapping of glacier ice/snow and the glacial lake has been performed. As a case study, Siachen glacier situated in the eastern Karakoram Range in the Himalayas has been mapped from Landsat 5 using OBIA approach. The rule base classification has been implemented by using eCognition Developer software.

关键词： multi-resolution segmentation Band Ratio NDWI NDVI Object-Based Image Analysis

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Regional topographic classification in the North Shaanxi Loess Plateau based on catchment boundary profiles

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PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT 2017年第3期41卷 302-324页

作者： Li, Min Yang, Xin Na, Jiaming Liu, Kai Jia, Yini Xiong, Liyang Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing Jiangsu Peoples R China State Key Lab Cultivat Base Geog Environm Evolut Nanjing Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing Jiangsu Peoples R China Wuxi Land & Resources Bur Surveying & Mapping Management Wuxi Peoples R China

Topographic classification and mapping are fundamental topics in geomorphology and have many promising applications. This study aimed to achieve landform classification using the catchment boundary profile (CBP) in the area of the North Shaanxi Province. CBPs were extracted, and seven indices were proposed and calculated to describe the profile features. Spatial maps of these indices were generated by interpolating 81 sample areas in the North Shaanxi Province of China. Finally, a topographic regional map was generated through multi-resolution segmentation after integrating the spatial maps of the seven indices. Results showed that the CBP indices were typical and representative to describe the terrain characteristics in the North Shaanxi Province and revealed their diverse spatial patterns. The investigated area was classified into 14 topographic units. The requirements of maximizing internal homogeneity while minimizing external homogeneity in terms of morphological characteristics were satisfied through the analysis of the terrain texture, topographic features, and land surface parameters. A comparison with the reference map showed that the regional classification differed from the geomorphologic map, given the different classification systems. However, the regional classification provided new knowledge of topographic features and spatial patterns in the macro scale. A comparison with other classification method indicated the advantage of the CBP-based method, which could distinguish the landforms of loess tableland, loess ridge, and loess hill that are the typical characteristics of loess landforms. The sensitivity analysis revealed that the differences in CBP indices, except for roughness and fractal dimension (FD) from different landforms, would increase with the digital elevation model (DEM) resolution becoming coarser, which indicates that the CBP-based method could be applied on a coarser resolution DEM if the differences of roughness and FD from different

关键词： Catchment boundary profile topographic classification multi-resolution segmentation digital elevation model loess plateau

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OBJECT-BASED RANDOM FOREST CLASSIFICATION OF LAND COVER FROM REMOTELY SENSED IMAGERY FOR INDUSTRIAL AND MINING RECLAMATION

OBJECT-BASED RANDOM FOREST CLASSIFICATION OF LAND COVER FROM...

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The ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing"

作者： Yuanpeng Chen Ming Luo Linlin Xu Xu Zhou Junjie Ren Ji Zhou Land Consolidation and Rehabilitation Center Ministry of Land and Resource China University of Geosciences(Beijing) School of Land Science and Technology

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16% in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58%. The classification accuracy of object-based RF classification was 1.42% higher than that of pixel-based classification(88.16%), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

关键词： Reclamation Area Classification of Land Use Random Forest Grid-search Object-based multi-resolution segmentation multi-feature Variables

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Advanced Techniques for Object-Based Classification of Hyperspectral Remote Sensing

Advanced Techniques for Object-Based Classification of Hyper...

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作者： SHAH,MAQSOOD 南京理工大学

学位级别：硕士

Land-Use Land-Cover(LULC)mapping is an important issue in agriculture *** demand for improved and more accurate LULC mapping has led to a methodology known as object-based image analysis(OBIA).The focus of this thesis is about the design and implementation of new methods for object-based(OB)classification of hyperspectral remote sensing *** earth observation instruments collect these ***,in this thesis we investigate the integration of methods for object-based hyperspectral classification in synergic *** images mapping is a crucial step in environmental monitoring,disaster management,agriculture management and military *** recent researches to improved and accurate hyperspectral mapping have led to key methodology known as Object-based classification of remotely sensed *** core intention of the OB images analysis is to group-of-pixels named "objects" through segmentation into a specific ***-oriented image analysis segments the data and constructs hierarchical network of homogeneous ***-based enable the analysis of aggregated sets of pixels,exploit shape-related variation,as well as spectral *** pixel-based classification methods always produce mixed pixel problems due to the independency of neighbor *** problem motivates researchers to combine segmentation,color and many other parameters to illuminate the wrongly classified or mixed pixels into their relevant *** the new methodology solved problems and improved accuracy,but it also raises new challenges such as the loss of accuracy in terms of less abundant,but potentially *** key challenges in hyperspectral images classification techniques are the high dimensionality of data,limited number of training samples,and combination of spatial and spectral *** recent years,availability of new remotely sensed data with high dimensions,spectral and spatial resolution rese

关键词： Land-use Land-Cover object-based image analysis support vector machines object-based multi-resolution segmentation Rotation-Based object-oriented principle component analysis rotation-matrix

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Change detection in very high resolution imagery and vector data applied to the monitoring of geographical conditions

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SENSOR REVIEW 2016年第4期36卷 347-358页

作者： Zhao, Zhenzhen Lin, Aiwen Yan, Qin Feng, Jiandi Wuhan Univ Wuhan Peoples R China Natl Adm Surveying Mapping & Geoinformat China Beijing Peoples R China

Purpose - Geographical conditions monitoring (GCM) has elicited significant concerns from the Chinese Government and is closely related to the "Digital China" program. This research aims to focus on object-based change detection (OBCD) methods integrating very-high-resolution (VHR) imagery and vector data for GCM. Design/methodology/approach - The main content of this paper is as follows: a multi-resolution segmentation (MRS) algorithm is proposed for obtaining homogeneous and contiguous image objects in two phases;a post-classification comparison (PCC) method based on the nearest neighbor algorithm and an image-object analysis (IOA) technique based on a differential entropy algorithm are used to improve the accuracy of the change detection;and a vector object-based accuracy assessment method is proposed. Findings - Results show that image objects obtained using the MRS algorithm attain the objectives of the "same spectrum within classes" and "different spectrum among classes". Moreover, the two OBCD methods can detect over 85 per cent of the changed regions. The PCC strategy can obtain the categories of image objects with a high degree of precision. The IOA technique is easy to use and largely automated. Originality/value - On the basis of the VHR satellite imagery and vector data, the above methods can effectively and accurately provide technical support for GCM implementation.

关键词： Remote sensing Change detection Geographical conditions monitoring multi-resolution segmentation Vector data

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