Geographic Object-based Image Analysis Research Articles

Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images

Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data.

Quantifying Intertidal Habitat Relative Coverage in a Florida Estuary Using UAS Imagery and GEOBIA

Intertidal habitats like oyster reefs and salt marshes provide vital ecosystem services including shoreline erosion control, habitat provision, and water filtration. However, these systems face significant global change as a result of a combination of anthropogenic stressors like coastal development and environmental stressors such as sea-level rise and disease. Traditional intertidal habitat monitoring techniques are cost and time-intensive, thus limiting how frequently resources are mapped in a way that is often insufficient to make informed management decisions. Unoccupied aircraft systems (UASs) have demonstrated the potential to mitigate these costs as they provide a platform to rapidly, safely, and inexpensively collect data in coastal areas. In this study, a UAS was used to survey intertidal habitats along the Gulf of Mexico coastline in Florida, USA. The structure from motion photogrammetry techniques were used to generate an orthomosaic and a digital surface model from the UAS imagery. These products were used in a geographic object-based image analysis (GEOBIA) workflow to classify mudflat, salt marsh, and oyster reef habitats. GEOBIA allows for a more informed classification than traditional techniques by providing textural and geometric context to habitat covers. We developed a ruleset to allow for a repeatable workflow, further decreasing the temporal cost of monitoring. The classification produced an overall accuracy of 79% in classifying habitats in a coastal environment with little spectral and textural separability, indicating that GEOBIA can differentiate intertidal habitats. This method allows for effective monitoring that can inform management and restoration efforts.

Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques

Coastal woodlands, notable for their floristic diversity and ecosystem service values, are increasingly under threat from a range of interacting biotic and abiotic stressors. Monitoring these complex ecosystems has traditionally been confined to field-scale vegetation surveys; however, remote sensing applications are increasingly becoming more viable. This study reports on the application of field-based monitoring and remote sensing/(Geographic Information System) GIS to interrogate trends in Banksia coastal woodland decline (Kings Park, Perth and Western Australia) and documents the patterns, and potential drivers, of tree mortality over the period 2012–2016. Application of geographic object-based image analysis (GEOBIA) at a park scale was of limited benefit within the closed-canopy ecosystem, with manual digitisation methods feasible only at the smaller transect scale. Analysis of field-based identification of tree mortality, crown-specific spectral characteristics and park-scale change detection imagery identified climate-driven stressors as the likely primary driver of tree mortality in the woodland, with vegetation decline exacerbated by secondary factors, including water stress and low system resilience occasioned by the inability to access the water table and competition between tree species. The results from this paper provide a platform to inform monitoring efforts using airborne remote sensing within coastal woodlands.

Land Cover Changes in Open-Cast Mining Complexes Based on High-Resolution Remote Sensing Data

Remote sensing technologies can play a fundamental role in the environmental assessment of open-cast mining and the accurate quantification of mine land rehabilitation efforts. Here, we developed a systematic geographic object-based image analysis (GEOBIA) approach to map the amount of revegetated area and quantify the land use changes in open-cast mines in the Carajás region in the eastern Amazon, Brazil. Based on high-resolution satellite images from 2011 to 2015 from different sensors (GeoEye, WorldView-3 and IKONOS), we quantified forests, cangas (natural metalliferous savanna ecosystems), mine land, revegetated areas and water bodies. Based on the GEOBIA approach, threshold values were established to discriminate land cover classes using spectral bands, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and a light detection and range sensor (LiDAR) digital terrain model and slope map. The overall accuracy was higher than 90%, and the kappa indices varied between 0.82 and 0.88. During the observation period, the mining complex expanded, which led to the conversion of canga and forest vegetation to mine land. At the same time, the amount of revegetated area increased. Thus, we conclude that our approach is capable of providing consistent information regarding land cover changes in mines, with a special focus on the amount of revegetation necessary to fulfill environmental liabilities.

On the versatility of popular and recently proposed supervised evaluation metrics for segmentation quality of remotely sensed images: An experimental case study of building extraction

Geographic Object-Based Image Analysis (GEOBIA) is a popular classification approach for high spatial resolution (HSR) images in the remote sensing (RS) community. It has been extensively reported that the final classification accuracy of GEOBIA is highly dependent on the quality of image segmentation. Different supervised, unsupervised, and post-classification quality assessment approaches have been proposed to evaluate whether a segmentation result has acceptable quality. Although there have recently been some theoretical review papers on supervised approaches in the context of RS, there have not been any comprehensively experimental tests on comparing these approaches for nearly a decade. Considering this gap, we experimentally compared different recently proposed and popular segmentation evaluation metrics to help the users narrow down the metrics that can be reliably used to measure the quality of a segmentation in a supervised manner. We assessed 21 distinguishable under-segmentation (US), over-segmentation (OS), and combined (UO) metrics in a two-step experimental procedure. The experiments showed that the OS metrics (including Recall, OSeg, NSR, and OSE) and US metrics (Precision, USeg, PSE, USE, and PI) each suffered from some problems including, but not limited to, unsuitable weighting (e.g. NSR), not enough sensitivity to segmentation variations (e.g. OSE/USE), uncertainty/unreliability in some cases (e.g. USeg/PSE), etc. Although none of the UO metrics was able to find the most optimal segmentation found with visual inspection, the UO metrics including F_measure, QR, and SEI could identify an optimal segmentation that was still of high quality and was very similar to the one visually found. It was also found that these three UO metrics performed very similar to each other, and that OI2 and AFI also led to similar results although AFI’s unbounded range of values is a source of confusion about what is the most reliable way to choose optimal segmentations in this approach.

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery.

High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.

A Histogram Curve-matching Approach for Object-based Image Change Analysis of Urban Land Use

Geographic object-based image analysis (GEOBIA) is commonly applied for land-cover and land-use mapping, updating and change-identification analyses. Following image segmentation, conventional GEOBIA routines classify image objects based on parametric statistical measures, assuming that within-object pixels have normally distributed image brightness signatures. The context for this study is updating extant land-use GIS layers that are out of date as a result of urban expansion. The objective is to develop, test and compare GEOBIA techniques based on a histogram classifier and on a nearest-neighbour classifier, for updating land-use layers. Frequency distribution signatures of land-use change and no-change objects are evaluated for different feature inputs and classifiers within an urbanizing area in San Diego County, California, USA. The results demonstrate that a histogram classifier consistently outperforms a conventional nearest-neighbour classifier. A Histogram Matching Root Sum Squared Differential Area classifier combined with temporal-spectral difference inputs and arithmetic mean for combining multi-feature classifier metrics yielded the greatest accuracy: 79.82% overall accuracy, with 78.72% and 81.07% for change and no-change objects respectively.

The use of combined Landsat and Radarsat data for urban ecosystem accounting in Canada

This paper describes an approach for combining Landsat and Radarsat satellite images to generate national statistics for urban ecosystem accounting. These accounts will inform policy related to the development of mitigation measures for climatic and hydrologic events in Canada. Milton, Ontario was used as a test case for the development of an approach identifying urban ecosystem types and assessing change from 2001 to 2019. Methods included decomposition of Radarsat images into polarimetric parameters to test their usefulness in characterizing urban areas. Geographic object-based image analysis (GEOBIA) was used to identify urban ecosystem types following an existing classification of local climate zones. Three supervised classifiers: decision tree, random forest and support vector machine, were compared for their accuracy in mapping urban ecosystems. Ancillary geospatial datasets on roads, buildings, and Landsat-based vegetation were used to better characterize individual ecosystem assets. Change detection focused on the occurrence of changes that can impact ecosystem service supply – i.e., conversions from less to more built-up urban types. Results demonstrate that combining Radarsat polarimetric parameters with the Landsat images improved urban characterization using the GEOBIA random forest classifier. This approach for mapping urban ecosystem types provides a practical method for measuring and monitoring changes in urban areas.

Object-specific optimization of hierarchical multiscale segmentations for high-spatial resolution remote sensing images

Accurate segmentation of high-spatial resolution remote sensing images remains a challenging problem for geographic object-based image analysis. An object-specific optimization method for hierarchical multiscale segmentations is proposed in this study by fusing multiple segmentations into an optimized segmentation with specific consideration of each object. Based on a segment tree model representing hierarchical multiscale segmentations, the framework of object-specific optimization is achieved by identifying and fusing the meaningful nodes in each path originating from a leaf node. Within the optimization framework, an optimization measure for identifying meaningful node is designed according to the maximum change of homogeneity in a path. The proposed optimization method is experimentally validated to hold the advantage of improving segmentation accuracy by the manner of object-specific optimization as well as the potential of automatically producing optimized segmentation for successive object-based analysis.

Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides.

Developing an Interpretation System for High-Resolution Remotely Sensed Images Based on Hybrid Decision-Making Process in a Multi-scale Manner

High spatial resolution (HSR) images are valuable data sources for urban applications. HSR images by high intra-class and low inter-class variabilities lead to a reduction in the statistical separability of the different land-cover classes in the spectral domain; therefore, conventional classification methods using merely spectral and textural information have proven to be inadequate for the HSR data. In this regard, interpretation systems by employing spatial and conceptual compatibility can be useful to closer a machining process to human analysis. In this research, a new framework has been introduced to classify remotely sensed imagery using a combination of ontological rules and geographic object-based image analysis. This paper to some degree attempts to untangle a few of the gaps in this field, especially by incorporating multi-scale analysis into the proposed framework, which is very advantageous in such an application. The present study used interactive segmentation and interpretation segmentation process with respect to the geometry of the image classes. In the proposed method, the segmentation process has been performed to avoid under-segmentation problems at several levels of the scale. The levels of scale are entered in the process of scoring and interpretation of the decision (not just applied at the level of results). This brings the process of labeling and interpretation closer to structural and natural reality. Additionally, a hybrid decision-making process (knowledge based and support vector machine) has been considered to achieve better results. The knowledge-based method is implemented to model the ontological relationships with the aim of labeling and controlling the decision-making process. To evaluate the efficiency of the method, the results of this research were assessed and compared with those of other methods in an urban area. The results showed that the proposed technique improved the overall accuracy and kappa coefficient by 9% and 11.5%, respectively.

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

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.

Geographic Object-Based Analysis of Airborne Multispectral Images for Health Assessment of Capsicum annuum L. Crops.

Vegetation health assessment by using airborne multispectral images throughout crop production cycles, among other precision agriculture technologies, is an important tool for modern agriculture practices. However, to really take advantage of crop fields imagery, specialized analysis techniques are needed. In this paper we present a geographic object-based image analysis (GEOBIA) approach to examine a set of very high resolution (VHR) multispectral images obtained by the use of small unmanned aerial vehicles (UAVs), to evaluate plant health states and to generate cropland maps for Capsicum annuum L. The scheme described here integrates machine learning methods with semi-automated training and validation, which allowed us to develop an algorithmic sequence for the evaluation of plant health conditions at individual sowing point clusters over an entire parcel. The features selected at the classification stages are based on phenotypic traits of plants with different health levels. Determination of areas without data dependencies for the algorithms employed allowed us to execute some of the calculations as parallel processes. Comparison with the standard normalized difference vegetation index (NDVI) and biological analyses were also performed. The classification obtained showed a precision level of about in discerning between vegetation and non-vegetation objects, and clustering efficiency ranging from to for the evaluation of different vegetation health categories, which makes our approach suitable for being incorporated at C. annuum crop’s production systems, as well as to other similar crops. This methodology can be reproduced and adjusted as an on-the-go solution to get a georeferenced plant health estimation.

GEOBIA Achievements and Spatial Opportunities in the Era of Big Earth Observation Data

The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis (GEOBIA), the primary focus was on integrating multiscale EO data with GIScience and computer vision (CV) solutions to cope with the increasing spatial and temporal resolution of EO imagery. Building on recent trends in the context of big EO data analytics as well as major achievements in CV, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic EO image analysis, and to identify opportunities where GEOBIA may support multi-source remote sensing analysis in the era of big EO data analytics. We (re-)emphasize the spatial paradigm as a key requisite for an image understanding system capable to deal with and exploit the massive data streams we are currently facing; a system which encompasses a combined physical and statistical model-based inference engine, a well-structured CV system design based on a convergence of spatial and colour evidence, semantic content-based image retrieval capacities, and the full integration of spatio-temporal aspects of the studied geographical phenomena.

Image classification based on the linear unmixing and GEOBIA

Geographic Object-Based Image Analysis and linear unmixing are common methods in image classification. The purpose of this study is to analyze the classification efficiency by integrating these two methods in the mountain area. This research selected Jiangle County, Fujian, as a study area. Two Landsat8 OLI images, which covered the county, were used. Linear spectral mixture model, multi-scale segmentation, and decision tree were applied in the classification. After image preprocessing, linear spectral mixture model was used to unmix the image into three fraction images-vegetation, shade, and soil. The principal component analysis and tasseled cap transformation were used to derived three principal components and the brightness, wetness, and greenness. Multi-scale segmentation is applied by eCognition. Under scale 40, the image was divided into vegetation and non-vegetation area, then under scale 20, the vegetation area was divided into different types by integrating the fraction with different methods. The accuracy assessment of the classification map was done using the forestry resource survey and the high-resolution image of Google Earth. This study indicated that the unmixed bands could improve the classification accuracy. The overall classification accuracy was 92.40% with a Kappa coefficient of 0.9032. Therefore, there is a conclusion that this approach is an efficient way to classify different plantation.

Using very high resolution (VHR) imagery within a GEOBIA framework for gully mapping: an application to the Calhoun Critical Zone Observatory

AbstractGully erosion is a form of accelerated erosion that may affect soil productivity, restrict land use, and lead to an increase of risk to infrastructure. An accurate mapping of these landforms can be difficult because of the presence of dense canopy and/or the wide spatial extent of some gullies. Even where possible, mapping of gullies through conventional field surveying can be an intensive and expensive activity. The recent widespread availability of very high resolution (VHR) imagery has led to a remarkable growth in the availability of terrain information, thus providing a basis for the development of new methodologies for analyzing Earth's surfaces. This work aims to develop a geographic object-based image analysis to detect and map gullies based on VHR imagery. A 1-meter resolution LIDAR DEM is used to identify gullies. The tool has been calibrated for two relatively large gullies surveyed in the Calhoun Critical Zone Observatory (CCZO) area in the southeastern United States. The developed procedure has been applied and tested on a greater area, corresponding to the Holcombe's Branch watershed within the CCZO. Results have been compared to previous works conducted over the same area, demonstrating the consistency of the developed procedure.

Su Kütlesi Belirlemede Farklı Sınıflandırma Yöntemlerinin Karşılaştırılması: Atikhisar Barajı (Çanakkale) Örneği

Water resources management is one of the most important issues of today. Satellite remote sensing have been successfully used to detect the presence of water bodies. In this study, four remote sensing methods: (1) normalized difference water index (NDWI), (2) support vector machine (SVM), (3) geographic object-based image analysis (GEOBIA) and (4) NDWI supported GEOBIA (GEOBIA_NDWI) were examined for water body area detection. For this purpose, Atikhisar Dam Lake, the only water source of Çanakkale central district of Turkey was selected as study area. As remote sensing data nine multitemporal Landsat-8 Operational Land Imager (OLI) multispectral satellite images between 2013 and 2017 were used. For the accuracy assessment, area values extracted from the used methods were tested with in-situ measurement lake area values. The main issues discussed in this study can be specified as follows: (i) Is pixel-based classification SVM or object-based image classification GEOBIA more successful in the water body detection?, (ii) Are the image classification methods (SVM and GEOBIA) or the water index (NDWI) more successful in the water body detection? and (iii) What is the contribution of NDWI to GEOBIA_NDWI (GEOBIA_NDWI) classification in the water body detection? The results show that meteorological factors and irrigation were influential in lake area variations. NDWI was found to be superior to other methods in determining water body and allowed for better detection of the lake boundary. Additionally, NDWI made a better separation of the land cover classes adjacent to water at the border. The object based GEOBIA was better than the pixel based SVM for distinguishing water and other land cover classes adjacent to border. GEOBIA_NDWI lake area results were more accurate than the standard object-based classification. Mixed pixels out of the lake area was determined less in the NDWI and GEOBIA_NDWI results.

Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis.

Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.

CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data

Traditional and convolutional neural network (CNN)-based geographic object-based image analysis (GeOBIA) land-cover classification methods prosper in remote sensing and generate numerous distinguished achievements. However, a bottleneck emerges and hinders further improvements in classification results, due to the insufficiency of information provided by very high-spatial resolution images (VHSRIs). To be specific, the phenomenon of different objects with similar spectrum and the lack of topographic information (heights) are natural drawbacks of VHSRIs. Thus, multisource data steps into people’s sight and shows a promising future. Firstly, for data fusion, this paper proposed a standard normalized digital surface model (StdnDSM) method which was actually a digital elevation model derived from a digital terrain model (DTM) and digital surface model (DSM) to break through the bottleneck by fusing VHSRI and cloud points. It smoothed and improved the fusion of point cloud and VHSRIs and thus performed well in follow-up classification. The fusion data then were utilized to perform multiresolution segmentation (MRS) and worked as training data for the CNN. Moreover, the grey-level co-occurrence matrix (GLCM) was introduced for a stratified MRS. Secondly, for data processing, the stratified MRS was more efficient than unstratified MRS, and its outcome result was theoretically more rational and explainable than traditional global segmentation. Eventually, classes of segmented polygons were determined by majority voting. Compared to pixel-based and traditional object-based classification methods, majority voting strategy has stronger robustness and avoids misclassifications caused by minor misclassified centre points. Experimental analysis results suggested that the proposed method was promising for object-based classification.

Optimizing multiscale segmentation with local spectral heterogeneity measure for high resolution remote sensing images

Image segmentation is a vital and fundamental step in Geographic Object-Based Image Analysis (GEOBIA). Many multiscale segmentation algorithms have been widely used in high resolution (HR) remote sensing images. These segmentation algorithms need a preset parameter, named scale parameter, to control the average size of each object. However, due to the spatial variation, single scale parameter can hardly describe the boundaries of regions with different land covers. To overcome this limitation, in this study, an adaptive parameter optimization method is proposed for multiscale segmentation. To find the optimal scale of objects, a local spectral heterogeneity measure is applied by calculating the spectral angle between inter and intra objects. Different from selecting a global optimal scale parameter, this study aims to directly search the optimal objects from results of all different scales and combine them into final segmentation results. In experiments, a multi-resolution segmentation is used to generate segmentation results of different scales and the QuickBird-2 images are used as test data. Optimization results over four HR test images reveal that the proposed method provides better segmentation performance than single scale segmentation result.