APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR MULTIRESOLUTION SEGMENTATION: A CASE STUDY IN A RIPARIAN MIXED FOREST

The increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. Multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.

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Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060420 ◽

2021 ◽

Vol 10 (6) ◽

pp. 420

Author(s):

Jun Wang ◽

Lili Jiang ◽

Qingwen Qi ◽

Yongji Wang

Keyword(s):

Parameter Optimization ◽

Scale Parameter ◽

Human Perception ◽

Optimization Methods ◽

Optimization Method ◽

Initiation Point ◽

Scale Parameters ◽

Object Based ◽

Geographic Object ◽

Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

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ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GREENHOUSES FROM WORLDVIEW-2 IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-145-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 145-152 ◽

Cited By ~ 1

Author(s):

M. A. Aguilar ◽

F. J. Aguilar ◽

A. García Lorca ◽

E. Guirado ◽

M. Betlej ◽

...

Keyword(s):

Image Objects ◽

Object Based Image Analysis ◽

Local Variance ◽

Sensing Applications ◽

Object Based ◽

Optimum Scale ◽

Values Assessment ◽

Segmentation Approach ◽

Optimal Setting ◽

Multiresolution Segmentation

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects. Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView- 2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR) and Euclidean Distance 2 (ED2).

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ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GREENHOUSES FROM WORLDVIEW-2 IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-145-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 145-152 ◽

Cited By ~ 13

Author(s):

M. A. Aguilar ◽

F. J. Aguilar ◽

A. García Lorca ◽

E. Guirado ◽

M. Betlej ◽

...

Keyword(s):

Image Objects ◽

Object Based Image Analysis ◽

Local Variance ◽

Sensing Applications ◽

Object Based ◽

Optimum Scale ◽

Values Assessment ◽

Segmentation Approach ◽

Optimal Setting ◽

Multiresolution Segmentation

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Optimizing the Segmentation of a High-Resolution Image by Using a Local Scale Parameter

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.7.503 ◽

2021 ◽

Vol 87 (7) ◽

pp. 503-511

Author(s):

Lei Zhang ◽

Hongchao Liu ◽

Xiaosong Li ◽

Xinyu Qian

Keyword(s):

Land Cover ◽

Scale Parameter ◽

Single Layer ◽

Remote Sensing Data ◽

Scale Parameters ◽

Object Based ◽

Segmentation Approach ◽

Optimal Segmentation ◽

Segment Matching

Image segmentation is a critical procedure in object-based identification and classification of remote sensing data. However, optimal scale-parameter selection presents a challenge, given the presence of complex landscapes and uncertain feature changes. This study proposes a local optimal segmentation approach that considers both intersegment heterogeneity and intrasegment homogeneity, uses the standard deviation and local Moran's index to explore each optimal segment across different scale parameters, and combines the optimal segments into a single layer. The optimal segment is measured by using high-spatial-resolution images. Results show that our approach out-performs and generates less error than the global optimal segmentation approach. The variety of land cover types or intrasegment homogeneity leads to segment matching with the geo-objects on different scales. Local optimal segmentation demonstrates sensitivity to land cover discrepancy and provides good performance on cross-scale segmentation.

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BACKSCATTER ANALYSIS USING MULTI-TEMPORAL SENTINEL-1 SAR DATA FOR CROP GROWTH OF MAIZE IN KONYA BASIN, TURKEY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-9-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 9-13 ◽

Cited By ~ 3

Author(s):

S. Abdikan ◽

A. Sekertekin ◽

M. Ustunern ◽

F. Balik Sanli ◽

R. Nasirzadehdizaji

Keyword(s):

Land Use ◽

Plant Height ◽

Stem Elongation ◽

Test Site ◽

Central Anatolia ◽

Crop Fields ◽

Object Based ◽

Multi Temporal ◽

Sar Imagery ◽

Konya Basin

Temporal monitoring of crop types is essential for the sustainable management of agricultural activities on both national and global levels. As a practical and efficient tool, remote sensing is widely used in such applications. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was utilized to investigate the performance of the sensor backscatter image on crop monitoring. Multi-temporal C-band VV and VH polarized SAR images were acquired simultaneously by in-situ measurements which was conducted at Konya basin, central Anatolia Turkey. During the measurements, plant height of maize plant was collected and relationship between backscatter values and plant height was analysed. The maize growth development was described under Biologische Bundesanstalt, bundessortenamt und CHemische industrie (BBCH). Under BBCH stages, the test site was classified as leaf development, stem elongation, heading and flowering in general. The correlation coefficient values indicated high correlation for both polarimetry during the early stages of the plant, while late stages indicated lower values in both polarimetry. As a last step, multi-temporal coverage of crop fields was analysed to map seasonal land use. To this aim, object based image classification was applied following image segmentation. About 80&thinsp;% accuracies of land use maps were created in this experiment. As preliminary results, it is concluded that Sentinel-1 data provides beneficial information about plant growth. Dual-polarized Sentinel-1 data has high potential for multi-temporal analyses for agriculture monitoring and reliable mapping.

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LANDSLIDE EXTRACTION FROM SENTINEL-2 IMAGE IN SIWALIK OF SURKHET DISTRICT, NEPAL

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-w2-9-2019 ◽

2019 ◽

Vol IV-5/W2 ◽

pp. 9-15

Author(s):

P. B. Budha ◽

A. Bhardwaj

Keyword(s):

Satellite Images ◽

Threshold Value ◽

Threshold Values ◽

Geometrical Properties ◽

Image Objects ◽

Object Based Image Analysis ◽

Loss And Damage ◽

Object Based ◽

Multiresolution Segmentation ◽

Sentinel 2

Abstract. Locating landslides and determining its extent is deemed an important task in estimating loss and damage and carry out mitigation works. As landslides are recurring phenomena in the research site, Siwalik Hills of western Nepal, freely available Sentinel-2 satellite images were considered to delineate landslides. The method employed in this process was Object-Based Image Analysis carried out in eCognition software using multiresolution segmentation algorithm. Parameters taken for segmentation were a scale of 20, the shape of 0.3, and compactness of 0.5. When a threshold value of < 0.35 in NDVI was used to distinguish landslides from image objects, some non-landslide objects were also selected. These false positives were removed successively using the threshold values on different bands, band ratios, slope information, hillshade and geometrical properties of image objects. There were altogether 264 landslides detected in the study area with size ranging from 300 m2 to 1675 m2 and landslide density of approximately 2 per km2. The accuracy, when compared to reference inventory, showed correctness and completeness measuring 80.28% and 66.27% respectively. These results showed semi-automatic landslide extraction was successful and Sentinel-2 can be used for similar tasks in other areas of Siwalik.

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COMBINATION OF SPECKLE DIVERGENCE AND NEIGHBORHOOD ANALYSIS TO CLASSIFY SETTLEMENT FROM TERASAR-X DATA

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2012.v9.a1820 ◽

2012 ◽

Vol 9 (1) ◽

Author(s):

Rokhis Komarudin ◽

Agung Indrajit

Keyword(s):

Specific Area ◽

Temporal Analysis ◽

Radar Data ◽

Tropical Country ◽

Neighborhood Analysis ◽

Object Based ◽

Radar Imagery ◽

Multi Temporal ◽

Sar Imagery ◽

Spatial Indexes

Abstract. The objectives of this research were to develop and improve methods for determination of settlements area with focus on synthetic aperture radar (SAR) data. Remote sensing settlement classification has made great progress, both for optical and radar data as well for their fusion. Yet, in radar imagery, settlement classification still contains some problems. Several studies on application of radar imagery have been conducted using techniques such as textural analysis, multi-temporal analysis, statistical model, spatial indexes, and object-based classification. Most of the development methods have several problems in the specific area especially in the tropical country. Several studies also showed that settlement classification accuracies were just below 60%. This was not sufficient enough to classify settlement areas using SAR imagery. Therefore, in this research, we proposed a new method i.e., the combination of the speckle divergence and the neighborhood analysis. The proposed method was applied to classify settlement area in Cilacap and Padang Districts of Indonesia. The results showed that the proposed method produced a good accuracy i.e., 85.5% for Cilacap Districts and 78.1% for Padang Districts.

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Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery

Remote Sensing ◽

10.3390/rs13132508 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2508

Author(s):

Loredana Oreti ◽

Diego Giuliarelli ◽

Antonio Tomao ◽

Anna Barbati

Keyword(s):

High Resolution ◽

Spatial Scales ◽

Parametric Method ◽

Mixed Forest ◽

Mixed Stands ◽

Mapping Unit ◽

Mixed Forests ◽

High Resolution Imagery ◽

Very High Resolution Imagery ◽

Very High

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

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A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

Remote Sensing ◽

10.3390/rs12060961 ◽

2020 ◽

Vol 12 (6) ◽

pp. 961 ◽

Cited By ~ 1

Author(s):

Marinalva Dias Soares ◽

Luciano Vieira Dutra ◽

Gilson Alexandre Ostwald Pedro da Costa ◽

Raul Queiroz Feitosa ◽

Rogério Galante Negri ◽

...

Keyword(s):

Remote Sensing Data ◽

Data Classification ◽

Test Site ◽

Radar Data ◽

Input Image ◽

Optical Data ◽

Land Use Classification ◽

Object Based ◽

Sar Imagery ◽

Small Change

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.

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A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽

10.3390/s19224893 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4893 ◽

Cited By ~ 22

Author(s):

Hejar Shahabi ◽

Ben Jarihani ◽

Sepideh Tavakkoli Piralilou ◽

David Chittleborough ◽

Mohammadtaghi Avand ◽

...

Keyword(s):

Machine Learning ◽

Image Segmentation ◽

Vegetation Index ◽

Scale Parameter ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Slope Length ◽

Optimal Scale ◽

Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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