Object‐based large‐scale terrain classification combined with segmentation optimization and terrain features: A case study in China

Na, Jiaming; Ding, Hu; Zhao, Wufan; Liu, Kai; Tang, Guoan; Pfeifer, Norbert

doi:10.1111/tgis.12795

Cited by 27 publications

(11 citation statements)

References 74 publications

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“…An overview of these features and the detailed description for each feature are listed in Table 1. As spectral and topographic features are widely used in the OBIA community [56,66,70,71], this information was firstly considered. For the spectral features, the R, G, and B bands from the images and a vegetation index based on the above-mentioned three features called EXG were considered.…”

Section: Image Segmentationmentioning

confidence: 99%

“…OBIA performs segmentation via a clustering method to determine the pixel groups belonging to a single meaningful object and then classifies the segmented objects [50]. Compared with pixel-based methods that do not use spatial concepts [51], the object-based method is more advanced, as it exploits the spatial information of target features, such as spectral, shape, and textural features [52][53][54][55][56]. Moreover, owing to the use of high-resolution data with high spectral variety between pixels, which often results in oversampling, the approach of OBIA of clustering pixels into objects is more effective than that of the pixel-based method [57] in processing high-resolution images requiring terrace damage extraction.…”

Section: Introductionmentioning

confidence: 99%

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OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry

Fang

Zhu

et al. 2021

IJGI

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Terraces, which are typical artificial landforms found around world, are of great importance for agricultural production and soil and water conservation. However, due to the lack of maintenance, terrace damages often occur and affect the local flow process, which will influence soil erosion. Automatic high-accuracy mapping of terrace damages is the basis of monitoring and related studies. Researchers have achieved artificial terrace damage mapping mainly via manual field investigation, but an automatic method is still lacking. In this study, given the success of high-resolution unmanned aerial vehicle (UAV) photogrammetry and object-based image analysis (OBIA) for image processing tasks, an integrated framework based on OBIA and UAV photogrammetry is proposed for terrace damage mapping. The Pujiawa terrace in the Loess Plateau of China was selected as the study area. Firstly, the segmentation process was optimised by considering the spectral features and the terrains and corresponding textures obtained from high-resolution images and digital surface models. The feature selection was implemented via correlation analysis, and the optimised segmentation parameter was achieved using the estimation of scale parameter algorithm. Then, a supervised k-nearest neighbourhood classifier was used to identify the terrace damages in the segmented objects, and additional geometric features at the object level were considered for classification. The comparison with the ground truth, as delineated by the image and field survey, showed that proposed classification can be adequately performed. The F-measures of extraction on three terrace damages were 92.07% (terrace sinkhole), 81.95% (ridge sinkhole), and 85.17% (collapse), and the Kappa coefficient was 85.34%. Finally, the potential application and spatial distribution of the terrace damages in this study were determined. We believe that this work can provide a credible framework for mapping terrace damages in the Loess Plateau of China.

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Section: Image Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry

Fang

Zhu

et al. 2021

IJGI

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“…Torrid research areas or subtopics in the OBIA approach related to segmentation are specific concepts of hierarchy and scale [17] [18] [19] [20] and [21]; segmentation of OBIA [9] [11] [22] [23] [24] [25] and [26]; OBIA change detection [7]; OBIA accuracy assessment [2] [27] [28] [29] [30] [31] and [32]; segmentation combined with classification [33] [34] and [35]; and Deep Learning combined with OBIA [9] [36] and [37]; recent uses of OBIA generally includes computer vision tasks as well as deep learning. OBIA applications, especially OBIA combined with trending methods, remain a vast area of research [38] [39] and [40].…”

Section: Introductionmentioning

confidence: 99%

Determination of Segmentation Parameters for Object-Based Remote Sensing Image Analysis from Conventional to Recent Approaches: A Review

2023

IJG

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Remote sensing has evolved through the appearance of several approaches. Object-based image analysis is a compelling approach to land use classification, object detection, and change detection in each environment. This paradigm is based on a critical and fundamental segmentation step. However, this step is highly dependent on the determination of the optimal parameters to be achieved. In this sense, methods have been invented to define the optimal segmentation parameters. This article presents an updated review of methods for defining optimal segmentation parameters. For this purpose, pertinent articles published in the main remote sensing journals from the emergence of the concept of object-based image analysis and segmentation to the present were used. The main aim is to provide a precise and detailed review of the different approaches previously presented. The originality of this review resides in the survey of all methods from conventional to the most recent with a discussion of these approaches. The results show that despite the advances in this field of research, most studies use the manual trial-and-error method. Conversely, state-of-the-art methods tend to determine the optimal parameter per type of geographic object and the adaptive calculation of segmentation parameters. Furthermore, the leading methods identified rely on supervised and unsupervised measures similarly, most of which use homogeneity measures. In contrast, a balance between intra- and inter-segment homogeneity and heterogeneity measures are more relevant. A distinction is made between pre-estimation and posterior parameter estimation methods.

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“…With the evolution of modern technology, artificial intelligence (AI) empowers the comprehensive optimisation and upgrading of map mapping from digitisation to intelligentisation. Thus, the process of integrating terrain features on various data (i.e., satellite and UAV images) (Drăguţ & Eisank, 2012; Na et al, 2021) with AI algorithms (i.e., machine learning, deep learning and ensemble learning) (Lin et al, 2020; Zhao et al, 2020; Zhou, Zhou, et al, 2021) to intelligently identify and classify surface landforms is the so‐called intelligent landform classification (Sunaga et al, 2019). Currently, automatic landform classification has achieved good results by means of combining AI and DEM data, for instance, incorporating the data‐ and feature‐level fusion into the object detection (Wang & Li, 2021).…”

Section: Introductionmentioning

confidence: 99%

Intelligent classification and analysis of regional landforms based on automatic feature selection

Xu,

Zhu,

et al. 2023

Earth Surf Processes Landf

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Terrain features are an important basis for realizing high‐precision landform classification, and feature selection is a key step of machine learning and knowledge mining. However, the feature selection process is facing challenges due to the multidimensionality and correlation of multisource terrain feature datasets and factors. Traditional feature selection methods lack enough consideration for the interpretability and transparency of feature factors, but the transparent decision‐making process of feature selection precisely determines the modelling effect and the reliability of model application results. Current research urgently needs to work out the black holes of visual representation during feature selection. In the process of intelligent landform classification, multiple and effective terrain feature is an essential factor in enhancing the performance and generalisation ability of the network. Therefore, we initially selected 40 terrain feature parameters, including basic terrain factors and digital elevation model (DEM) terrain textures, to calculate the feature contribution degree and sort the parameter importance based on the SHapley Additive exPlanations (SHAP) method, then reserved 10%, 20%, 30%, 40% and 50% terrain features in turn for constructing the landform classification dataset. Because the traditional UNet network cannot completely capture abrupt landform features, the convolutional block attention module (CBAM) was integrated into the UNet, and a deep learning model was established for the fine‐grained classification of regional landforms. Considering the calculation rate, even though there are large regional spatial differences and genetic mechanisms, it is appropriate to retain 20% of terrain features for intelligent landform classification. The classification accuracy of typical regions, namely, the Hanzhong Basin, North China Plain, Yunnan–Guizhou Plateau and Tibetan Plateau, reached 98.76%, 97.36%, 96.3% and 92.78%, respectively, and what's more, some accuracies went up to a higher level under other feature combinations. Meanwhile, given the different feature combinations corresponding to regional landform types, the combinative stability and spatial orderliness characteristics were explored to explain the accuracy variation trend.

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Object‐based large‐scale terrain classification combined with segmentation optimization and terrain features: A case study in China

Cited by 27 publications

References 74 publications

OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry

OBIA-Based Extraction of Artificial Terrace Damages in the Loess Plateau of China from UAV Photogrammetry

Determination of Segmentation Parameters for Object-Based Remote Sensing Image Analysis from Conventional to Recent Approaches: A Review

Intelligent classification and analysis of regional landforms based on automatic feature selection

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