Optimum Scale in Object‐Based Image Analysis

Im, Jungho; Quackenbush, Lindi J.; Li, Manqi; Fang, Fang

doi:10.1002/9781118801628.ch10

Cited by 17 publications

(6 citation statements)

References 85 publications

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“…The Trimble's eCognition software was used to segment the orthoimage image. Segmentation parameters the scale (50), shape (0.2) and compactness (0.5) parameters were carefully and manually selected such that they gave visually appealing segmentation results across the majority of the orthoimage following the common practice for selecting segmentation parameters for OBIA [48]. This process resulted in 40,239 objects within the study area.…”

Section: Orthoimage Creation and Segmentationmentioning

confidence: 99%

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Liu

Abd-Elrahman

2018

Remote Sensing

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Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, multi-view data should be considered for training data enrichment, which has not been investigated for FCN. The present study developed a novel OBIA classification using FCN and multi-view data extracted from small Unmanned Aerial System (UAS) for mapping landcovers. Specifically, this study proposed three methods to automatically generate multi-view training samples from orthoimage training datasets to conduct multi-view object-based classification using FCN, and compared their performances with each other and also with RF, SVM, and DCNN classifiers. The first method does not consider the object surrounding information, while the other two utilized object context information. We demonstrated that all the three versions of FCN multi-view object-based classification outperformed their counterparts utilizing orthoimage data only. Furthermore, the results also showed that when multi-view training samples were prepared with consideration of object surroundings, FCN trained with these samples gave much better accuracy than FCN classification trained without context information. Similar accuracies were achieved from the two methods utilizing object surrounding information, although sample preparation was conducted using two different ways. When comparing FCN with RF, SVM, DCNN implies that FCN generally produced better accuracy than the other classifiers, regardless of using orthoimage or multi-view data.Remote Sens. 2018, 10, 457 2 of 24 all the UAS images. Then, image segmentation algorithm is conducted to segment the orthoimage to groups of homogeneous pixels to form numerous meaningful objects. Spectral, geometrical, textural, and contextual features are extracted from these objects and used as input to different classifiers, such as Random Forest (RF) [9] and Support Vector Machine (SVM) [10], to label the objects. Feature extraction and selection that have to be conducted during traditional OBIA procedures are challenging tasks and can limit classification performance.Recently, the rise of deep learning techniques provided an alternative to traditional land cover classifiers. Deep learning brought about around 2006 [11], became well known in the computer vision community around 2012, since one supervised version of deep learning networks Deep Convolutional Neural Networks (DCNN) made a breakthrough for scene classification tasks [12,13], and has reached out to many industrial applications and other academic areas in recent years as it continues to advance technologies in areas, like speech recognition [14], medical diagnosis [15], autonomous driving [16], or even the gaming world [17,18]. When compared with other traditional classifiers, deep learning does not require feature e...

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Section: Orthoimage Creation and Segmentationmentioning

confidence: 99%

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Liu

Abd-Elrahman

2018

Remote Sensing

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“…A disadvantage is the amount of time taken to select the most suitable features to get a good performance from a machine learning classifier [19]. In addition, the variability in plot sizes and shapes means that certain configuration parameters do not allow for the proper delineation and classification of all agricultural plots in a scene [20].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System

et al. 2019

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Accurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots. INDEX TERMS Convolutional neural network, deep learning, edge extraction, land parcel identification system, parcels delineation.

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“…The exhaustive method is often employed for selecting an optimal segmentation threshold (OST) based on quantitative assessment and ranking with respect to an exhaustive segment data stack (Arvor et al, 2013;Im et al, 2014;Marpu et al, 2010;Myint et al, 2011). An exhaustive segment data stack consists of tiles of segment datasets, and each tile is related to a threshold.…”

Section: Introductionmentioning

confidence: 99%

Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis

Liu¹,

Zhang²,

Huang³

et al. 2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:To create image objects for subsequent classification in object-based image analysis, an optimal segmentation threshold (OST) is a pre-requisite for image segmentation. However, an OST is practically acquired by assessing and ranking an exhaustive segment data stack constructed after foregoing image segmentation. In this paper, we propose an iterative exploration method via recognition of the Euclidean distance 2 (ED2)-scale parameter (SP) pattern with the least five tiles of segment data stack in each cycle among the Potential Segmentation Error (PSE)-Number-of-Segments Ratio (NSR)-ED2 patterns along with SP. We conducted two experiments. In the first experiment, we validated the general italic U-shaped ED2-SP pattern by constructing exhaustive segment data stacks and corresponding segment data stacks. In the second experiment, we adopted the proposed iterative exploration method for OST selection based on the ED2-SP pattern with respect to five equal-spacing SPs in each cycle. The bottom of the pattern was persistently approached by constructing updated segment data stacks and corresponding segment data stacks with five dynamically adjusted tiles. Our results showed that the PSE-NSR-ED2 discrepancy measure system is advantageous to OST selection.

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Optimum Scale in Object‐Based Image Analysis

Cited by 17 publications

References 85 publications

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System

Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis

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