Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data

Lourenço, Patrícia; Teodoro, Ana Cláudia; Gonçalves, J.; Honrado, João P.; Cunha, Mário; Sillero, Neftalí

doi:10.1016/j.jag.2020.102263

Cited by 38 publications

(23 citation statements)

References 56 publications

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“…Nonetheless, a further reduction of the number of used software packages is rather unlikely; we are not aware of a package that can perform both OBIA and photogrammetric processing. Additionally, eCognition, the software we used for OBIA, achieves more accurate results than other available software packages such as OTB/Monteverdi and ArcGIS [57]. Moreover, eCognition enables to perform cleaning and filtering the image before classification, which improved substantially our results.…”

Section: Discussionmentioning

confidence: 85%

Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

et al. 2021

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Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity.

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Section: Discussionmentioning

confidence: 85%

Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

et al. 2021

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“…By segmentation processing the image would be segmented into a spatially contiguous objects set, every object in the set is composed of a certain number of pixels with homogeneity [ 17 ]. The multiresolution algorithm (MRS) was used for image segmentation; this algorithm is widely used in image segmentation studies [ 35 , 36 , 37 ].…”

Section: Methodsmentioning

confidence: 99%

“…Object-based image analysis (OBIA) is a method of processing high-resolution images [ 12 , 13 , 14 , 15 , 16 , 17 ]. OBIA divides the image into several coessential but non-overlapping regions, and the regions are processed as objects.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

Hao

Cui

Wang

2021

Sensors

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High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

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“…The natural color composite images (Band RGB 432) were used as the input image layer (IIL) in the OBIA classification process. The segmentation process uses the Mean Shift Segmentation (MSS) algorithm (Lourenço et al 2021). MSS parameters consist of spectral detail, spatial detail, and minimum segment size.…”

Section: Shallow Waters Benthic Habitat Mapping and Accuracy Testmentioning

confidence: 99%

Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu, Indonesia

Hartoni

Siregar

Wouthuyzen³

et al. 2022

Biodiversitas

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Abstract. Hartoni, Siregar VP, Wouthuyzen S, Agus SB. 2021. Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu. Biodiversitas 23: 514-520. Benthic habitats have very high complexity and are home to many types of aquatic organisms. Benthic habitats have various functions, including habitat for flora and fauna, sediment traps, nursery areas, and foraging areas for aquatic fauna that are susceptible to damage due to human activities or natural factors. Therefore, more accurate spatial information is needed. The purpose of this study was to examine the ability of object-based classification techniques for mapping shallow waters benthic habitats using Sentinel 2A imagery. The two classification algorithms used are support vector machine (SVM) and random forest (RF). The input image layer (IIL) used for classification is the natural color band (Band 432). The results showed that the SVM and RF classification algorithms could classify eight classes of benthic habitats. The overall accuracy (OA) of the SVM algorithm is 65%, while the RF accuracy is 67%, with kappa values of 0.59 and 0.60, respectively. The significant test applied to Sentinel 2 images with SVM and RF algorithms for benthic habitats has a Z test value of-0.41. These results indicate that the classification results between the SVM and RF algorithms are not significantly different.

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Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data

Cited by 38 publications

References 56 publications

Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu, Indonesia

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