Object-based Image Classification Research Articles

Monitoring Onion Crop “Cipolla Rossa di Tropea Calabria IGP” Growth and Yield Response to Varying Nitrogen Fertilizer Application Rates Using UAV Imagery

Remote sensing (RS) platforms such as unmanned aerial vehicles (UAVs) represent an essential source of information in precision agriculture (PA) as they are able to provide images on a daily basis and at a very high resolution. In this framework, this study aims to identify the optimal level of nitrogen (N)-based nutrients for improved productivity in an onion field of “Cipolla Rossa di Tropea” (Tropea red onion). Following an experiment that involved the arrangement of nine plots in the onion field in a randomized complete block design (RCBD), with three replications, three different levels of N fertilization were compared: N150 (150 kg N ha−1), N180 (180 kg N ha−1), and e N210 (210 kg N ha−1). The crop cycle was monitored using multispectral (MS) UAV imagery, producing vigor maps and taking into account the yield of data. The soil-adjusted vegetation index (SAVI) was used to monitor the vigor of the crop. In addition, the coverage’s class onion was spatially identified using geographical object-based image classification (GEOBIA), observing differences in SAVI values obtained in plots subjected to differentiated N fertilizer treatment. The information retrieved from the analysis of soil properties (electrical conductivity, ammonium and nitrate nitrogen), yield performance and mean SAVI index data from each field plot showed significant relationships between the different indicators investigated. A higher onion yield was evident in plot N180, in which SAVI values were higher based on the production data.

Evaluation and Monitoring of Urban Public Greenspace Planning Using Landscape Metrics in Kunming

Urban greenspace planning plays a crucial role in improving the quality of human settlements and the living standard of citizens. Urban public greenspace (UPGS) is an important part of urban greenspaces. Existing literature rarely includes a scientific evaluation of greenspace plans (including of UPGS) and plan implementation effects. To bridge this gap, this study evaluated and monitored the UPGS plan enacted in 2010 in Kunming, China. Object-based image classification and visual interpretation of satellite images and Google Earth imagery were used to quantify the different periods of UPGS implementation. Six indicators and monitoring at four classic sites were applied to explore the change at two scales (overall scale and district scale) for monitoring the UPGS plan execution. The results showed that UPGS structure greatly improved after plan implementation. However, UPGS provision per capita has not reached the level of greenspace planning and the connectivity was poor. Significant implementation inequalities existed in each district and implementation has lagged behind schedule. This study contributes to a better understanding of greenspace planning and urban planning in general, which can help improve future planning and planning decisions.

3D Classification of Cold-Water Coral Reefs: A Comparison of Classification Techniques for 3D Reconstructions of Cold-Water Coral Reefs and Seabed

Cold-water coral (CWC) reefs are complex structural habitats that are considered biodiversity “hotspots” in deep-sea environments and are subject to several climate and anthropogenic threats. As three-dimensional structural habitats, there is a need for robust and accessible technologies to enable more accurate reef assessments. Photogrammetry derived from remotely operated vehicle video data is an effective and non-destructive method that creates high-resolution reconstructions of CWC habitats. Here, three classification workflows [Multiscale Geometrical Classification (MGC), Colour and Geometrical Classification (CGC) and Object-Based Image Classification(OBIA)] are presented and applied to photogrammetric reconstructions of CWC habitats in the Porcupine Bank Canyon, NE Atlantic. In total, six point clouds, orthomosaics, and digital elevation models, generated from structure-from-motion photogrammetry, are used to evaluate each classification workflow. Our results show that 3D Multiscale Geometrical Classification outperforms the Colour and Geometrical Classification method. However, each method has advantages for specific applications pertinent to the wider marine scientific community. Results suggest that advancing from commonly employed 2D image analysis techniques to 3D photogrammetric classification methods is advantageous and provides a more realistic representation of CWC habitat composition.

Damage Assessment and Spatio-Temporal Mapping Using Object Based Image Analysis Technique on Floods-2015 in Southern Punjab, Pakistan

Floods are the natural disasters which not only harm human lives but also damage entire structure in the inundated area. Pakistan has been witnessing extensive floods since very long because of long and severe spells of monsoon rainfalls with abrupt diurnal and seasonal disparity in the temperature level every year. Floods of 2010 and 2014 were so severe that they had broken the disaster record of earthquake-2005 in Pakistan and floods of 2015 had devastated the southern Punjab badly. These floods have damaged socioeconomic activities and people were displaced from the flood prone areas. Southern Punjab has been facing this calamity frequently from 2010 onward which resulting to huge destruction and gigantic loss of human lives every year. An immaculate large area of the Southern Punjab was damaged with crop land, built-up areas and road networks. To assess the damages and spatio-temporal mapping during flood period, remote sensing and GIS techniques have been incorporated by using spatial statistical and object-based image classification techniques. These techniques have been applied for estimating damages, mapping and extracting flood extents by embedding NDVI, NDBI and NDWI methods into object-based image analysis. This paper concludes assessment of damages, various extents of flood and accuracy assessment (79.3% and Khat0.72). Outcomes of the paper would be very beneficial for disaster management authorities for mitigating future floods and helpful for flood monitoring departments to guide competent management authorities at all tiers for quick response and rehabilitation programmes.

A phenological object-based approach for rice crop classification using time-series Sentinel-1 Synthetic Aperture Radar (SAR) data in Taiwan

ABSTRACT Spatial assessment of rice-cultivated areas is a crucial activity in Taiwan due to government initiatives to provide policymakers with reliable and timely information for devising successful rice grain import and export plans. The objective of this study is to develop a phenological object-based approach for collectively mapping patches of rice fields using the time-series Sentinel-1 Synthetic Aperture Radar (SAR) data. We processed the data for 2019 cropping seasons, following three main steps: (1) data pre-processing to construct the smooth twelve-day time-series composite vertical transmit and horizontal receive (VH) polarized data, (2) object-based image classification of rice-cultivated areas using phenological metrics, and (3) accuracy assessment of the mapping results. The classification maps compared with the government’s ground reference maps indicated satisfactory accuracies, with producer’s accuracies of 84.2% and 82.6% and user’s accuracies of 82.1% and 85.3% for the first and second crops, respectively. These results were reaffirmed by close agreement in area estimates between the satellite-derived rice area and government’s reference data at township level, with coefficient of determination (R 2) values of 0.96 and 0.94 for the first and second crops, respectively. In spite of some error sources, including mixed-pixel issues and edge effects, that lowered the mapping accuracy, the results of this study have demonstrated that our mapping approach using the time-series Sentinel-1 VH data and information of crop phenology could be potentially applied at a larger scale in Taiwan and transferable to other regions for updating rice crop maps on a timely and frequent basis.

Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery

ABSTRACT This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images’ T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10−5. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape.

Mapping Soil Degradation on Arable Land with Aerial Photography and Erosion Models, Case Study from Danube Lowland, Slovakia

The presented study uses the recent colour aerial photographs, historical black and white aerial photographs, and detailed digital elevation model to assess the spatial distribution and long-term temporal dynamics of soil loss in agriculturally intensively exploited loess hilly land with a subcontinental temperate climate. The strongly eroded soils appear in the studied area as bright patterns, surrounded by darker soils, and they are well visible on aerial photos. Three approaches of interpretation of aerial photographs were tested: visual interpretation, pixel-based image classification, and object-based image classification. All three methods provided detailed maps of soil redistribution patterns. The bright areas as the areas of soil degradation characterized by erosion increased from 1949 until 2011 by 76%. A detailed map of areal erosion patterns was used for the validation of water erosion models. LS-factor of USLE and ED’ index of USPED were selected for expressing the relation of real erosion to the terrain. The relationship between surface morphology and real erosion is very complex, and the tested water erosion models do not express it sufficiently. Therefore, the first and second-order directional derivative of the surface elevations with respect to the tillage direction has been tested. The absolute value of the first-order directional derivative showed better results and better corresponded with the real erosion pattern than the other morphometric characteristics. The findings suggest that tillage is the dominant erosion factor in the area.

Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

A Socio-Spatial Analysis of Land Use Dynamics and Process of Land Intervention in the Peri-Urban Areas of Bahir Dar City

The contemporary urbanization and its implication to land use dynamics especially in the peri-urban areas are emerging as a cross-cutting theme in policy debates and scientific discourse. As most cities in developing countries, including Ethiopia, are experiencing continuous expansion of built-ups and dynamic land use changes, monitoring and an in-depth analysis of the past, present and future predictions of these changes are important for a holistic understanding of the problem, its consequence, and to regulate proper land use intervention options. Thus, the main objective of this research is to assess land use dynamics and processes of land intervention in the peri-urban areas of Bahir Dar city using a socio-spatial analysis. It assesses to what extent the existing peri-urban land intervention processes and land use decisions are effective in combating and controlling unwanted land use changes. Primary socio-economic data were collected using questionnaires, focus group discussions (FGDs) and key informant interviews; in addition, spatial data including Landsat and Sentinel imageries of 1993, 2001, 2011 and 2020 were utilized. Land use/land cover (LULC) classes were computed using the integration of spectral and object-based image classification techniques. The results signal that built-ups are expanding horizontally with unpredicted patterns. This is because the existing land intervention processes are lacking effectiveness to govern the spatial patterns of built-ups. The results further depict that processes of land use intervention do not only determine horizontal urban expansion but also determine the nature of people-to-land relationships, which involve both formal and informal processes. This creates haphazard, disputed and unregulated land use systems in peri-urban areas of Bahir Dar. The socio-spatial methodology applied in this research is effective in monitoring both the spatial and social dimensions of land use changes. The spatial results effectively demonstrate the dynamics of land uses; whereas, the social analysis supports understanding of the processes of land use interventions. In conclusion, monitoring processes of land use interventions are key policy and decision making directions to regulate and manage land use dynamics in the peri-urban area.

A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the ‘Cipolla Rossa di Tropea’ (Italy)

Precision agriculture (PA) is a management strategy that analyzes the spatial and temporal variability of agricultural fields using information and communication technologies with the aim to optimize profitability, sustainability, and protection of agro-ecological services. In the context of PA, this research evaluated the reliability of multispectral (MS) imagery collected at different spatial resolutions by an unmanned aerial vehicle (UAV) and PlanetScope and Sentinel-2 satellite platforms in monitoring onion crops over three different dates. The soil adjusted vegetation index (SAVI) was used for monitoring the vigor of the study field. Next, the vigor maps from the two satellite platforms with those derived from UAV were compared by statistical analysis in order to evaluate the contribution made by each platform for monitoring onion crops. Besides, the two coverage’s classes of the field, bare soil and onions, were spatially identified using geographical object-based image classification (GEOBIA), and their spectral contribution was analyzed comparing the SAVI calculated considering only crop pixels (i.e., SAVI onions) and that calculated considering only bare soil pixels (i.e., SAVI soil) with the SAVI from the three platforms. The results showed that satellite imagery, coherent and correlated with UAV images, could be useful to assess the general conditions of the field while UAV permits to discriminate localized circumscribed areas that the lowest resolution of satellites missed, where there are conditions of inhomogeneity in the field, determined by abiotic or biotic stresses.

Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation

Sparse representation (SR)-based models have been widely applied for hyperspectral image classification. In our previously established constraint representation (CR) model, we exploited the underlying significance of the sparse coefficient and proposed the participation degree (PD) to represent the contribution of the training sample in representing the testing pixel. However, the spatial variants of the original residual error-driven frameworks often suffer the obstacles to optimization due to the strong constraints. In this paper, based on the object-based image classification (OBIC) framework, we firstly propose a spectral–spatial classification method, called superpixel-level constraint representation (SPCR). Firstly, it uses the PD in respect to the sparse coefficient from CR model. Then, transforming the individual PD to a united activity degree (UAD)-driven mechanism via a spatial constraint generated by the superpixel segmentation algorithm. The final classification is determined based on the UAD-driven mechanism. Considering that the SPCR is susceptible to the segmentation scale, an improved multiscale superpixel-level constraint representation (MSPCR) is further proposed through the decision fusion process of SPCR at different scales. The SPCR method is firstly performed at each scale, and the final category of the testing pixel is determined by the maximum number of the predicated labels among the classification results at each scale. Experimental results on four real hyperspectral datasets including a GF-5 satellite data verified the efficiency and practicability of the two proposed methods.

Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches

Object-based image analysis (OBIA) has been widely used to classify high spatial resolution (HSR) imagery. In a traditional OBIA, object-level statistical summaries such as mean values are usually used for classification. This implies that the spectral values within objects follow a Gaussian distribution. However, the pixel values in an object do not necessarily conform to a Gaussian distribution because of within object spectral heterogeneity. Consequently, these statistical summaries may misrepresent the features of the object. This shortcoming is addressed in this paper by integrating both the spectral variability and the spatial distribution of the pixels within objects to improve the traditional object-based image classification. The spectral variability is represented by histograms of the pixel values in the object, and the spatial distribution is characterized by the binary spatial covariogram of these pixels. To construct a binary spatial covariogram, a principal component analysis (PCA) is first applied to compress multiple bands into one, and the Otsu thresholding is then performed to generate a binary map reflecting the spatial configuration of the pixels. Spatial covariance is then computed for this binary map and plotted with different lag distances to derive the binary spatial covariogram. Our proposed model utilizing curves composed of the spectral histograms and binary spatial covariogram (referred to as the His-Cov model) are then used for classification based on curve matching approaches. The integration of spectral variability and spatial distribution of the pixels in the object produced superior results to curve matching approaches based on spectral variability alone and to traditional OBIA based on spectral and spatial features of the objects when classifying complex land use types in urban environments.

Ultra-High-Resolution Mapping of Posidonia oceanica (L.) Delile Meadows through Acoustic, Optical Data and Object-based Image Classification

In this study, we present a framework for seagrass habitat mapping in shallow (5–50 m) and very shallow water (0–5 m) by combining acoustic, optical data and Object-based Image classification. The combination of satellite multispectral images-acquired from 2017 to 2019, together with Unmanned Aerial Vehicle (UAV) photomosaic maps, high-resolution multibeam bathymetry/backscatter and underwater photogrammetry data, provided insights on the short-term characterization and distribution of Posidonia oceanica (L.) Delile, 1813 meadows in the Calabrian Tyrrhenian Sea. We used a supervised Object-based Image Analysis (OBIA) processing and classification technique to create a high-resolution thematic distribution map of P. oceanica meadows from multibeam bathymetry, backscatter data, drone photogrammetry and multispectral images that can be used as a model for classification of marine and coastal areas. As a part of this work, within the SIC CARLIT project, a field application was carried out in a Site of Community Importance (SCI) on Cirella Island in Calabria (Italy); different multiscale mapping techniques have been performed and integrated: the optical and acoustic data were processed and classified by different OBIA algorithms, i.e., k-Nearest Neighbors’ algorithm (k-NN), Random Tree algorithm (RT) and Decision Tree algorithm (DT). These acoustic and optical data combinations were shown to be a reliable tool to obtain high-resolution thematic maps for the preliminary characterization of seagrass habitats. These thematic maps can be used for time-lapse comparisons aimed to quantify changes in seabed coverage, such as those caused by anthropogenic impacts (e.g., trawl fishing activities and boat anchoring) to assess the blue carbon sinks and might be useful for future seagrass habitats conservation strategies.

LAND USE AND LAND COVER MAPPING – A CASE STUDY OF AHMEDABAD DISTRICT

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

Comparative Analysis of Pixel and Object Based Classification Approach for Rapid Landslide Delineation with the Aid of Open Source Tools in Garhwal Himalaya

Abstract Landslides are the natural geomorphic processes that are essential for the landscape development. Loss of tremendous amount of life, natural resources as well as property has made this phenomenon a natural disaster. Number of researches have been done to understand this phenomenon and how to overcome this calamity with the aid of upcoming modern information services and technological advancements. Despite all these, there are still certain undocumented slope failure events due to inaccessibility and lack of a proper database. With the advancement of satellite remote sensing and geographical information system (GIS), it has certainly become easier to monitor and prepare landslide database especially in rough and rugged terrain of the Himalaya for delineation of risk zones. Free availability of high-resolution images and an open source efficient software have certainly been proved advantageous for this purpose. This paper aims at quick and accurate landslide inventory mapping, using high resolution Sentinel 2 data along with normalized difference vegetation index (NDVI), over which unsupervised and object-based image analysis (OBIA) was done to extract landslide features in an efficient manner. Both these processes were achieved with the aid of open source SAGA (System for Automated Geoscientific Analyses) software. The performances of these classifications were analyzed for their quantitative reliability in extracting the landslide features in two different areas (test area 1 and 2). In this, the object based image classification outstands, with the overall accuracy percentage difference of 17.11% higher than pixel-based classification approach in test area 1 and about 21% higher in test area 2.

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.

Object-Based Classification Approaches for Multitemporal Identification and Monitoring of Pastures in Agroforestry Regions using Multispectral Unmanned Aerial Vehicle Products

Sown Biodiverse Pastures (SBP) are the basis of a high-yield grazing system tailored for Mediterranean ecosystems and widely implemented in Southern Portugal. The application of precision farming methods in SBP requires cost-effective monitoring using remote sensing (RS). The main hurdle for the remote monitoring of SBP is the fact that the bulk of the pastures are installed in open Montado agroforestry systems. Sparsely distributed trees cast shadows that hinder the identification of the underlaying pasture using Unmanned Aerial Vehicles (UAV) imagery. Image acquisition in the Spring is made difficult by the presence of flowers that mislead the classification algorithms. Here, we tested multiple procedures for the geographical, object-based image classification (GEOBIA) of SBP, aiming to reduce the effects of tree shadows and flowers in open Montado systems. We used remotely sensed data acquired between November 2017 and May 2018 in three Portuguese farms. We used three machine learning supervised classification algorithms: Random Forests (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). We classified SBP based on: (1) a single-period image for the maximum Normalized Difference Vegetation Index (NDVI) epoch in each of the three farms, and (2) multi-temporal image stacking. RF, SVM and ANN were trained using some visible (red, green and blue bands) and near-infrared (NIR) reflectance bands, plus NDVI and a Digital Surface Model (DSM). We obtained high overall accuracy and kappa index (higher than 79% and 0.60, respectively). The RF algorithm had the highest overall accuracy (more than 92%) for all farms. Multitemporal image classification increased the accuracy of the algorithms. as it helped to correctly identify as SBP the areas covered by tree shadows and flower patches, which would be misclassified using single image classification. This study thus established the first workflow for SBP monitoring based on remotely sensed data, suggesting an operational approach for SBP identification. The workflow can be applied to other types of pastures in agroforestry regions to reduce the effects of shadows and flowering in classification problems.

Characterizing the Landscape Structure of Urban Wetlands Using Terrain and Landscape Indices

Several studies have shown human impacts on urban wetlands. These impacts are mostly studied at broad scales, which may generalize and aggregate important information needed for landscape quantification or terrain analysis. This situation can weakly or inappropriately address the structure of wetland landscapes, thus affecting the assessment of the quantities and qualities of terrestrial wetland habitats. To address these issues for urban wetland dynamics, this study proposes the use of landscape and terrain indices to characterize the landscape structure of urban wetlands at a fine scale in order to assess its usefulness in contributing to wildlife sustainability. To achieve this goal, secondary terrain attribute data are integrated with an object-based satellite image classification at the wetland and watershed level. The result reveals a general swell in wetland coverage at the watershed level. Further analysis shows the size and shape complexities, and edge irregularities are increased significantly at the patch level but slightly at the watershed level. Terrain analysis further reveals a potential increase in wetness and decrease in stream power vulnerability for most of the major wetlands under study. These results suggest that terrain and landscape indices are effective in characterizing the structure of urban wetlands that supports socio-ecological sustainability.

Object-Based Classification of PolSAR Images Based on Spatial and Semantic Features

High-resolution polarimatric synthetic aperture radar (PolSAR) images can provide more detail information on land-cover types and increase the image complexity at the same time. Traditionally, pixel-based image classification that takes image pixel as a processing unit cannot make full use of various features contained in high-resolution remote sensing images, and thus may not obtain satisfactory results. Hence, object-based image classification (OBIC) methods using image objects as processing units have been introduced into the PolSAR image classification. However, current researches on OBIC methods for PolSAR images usually could not take advantage of multiscale information of image objects, leading to some results that are not as satisfactory as expected. In this article, a multilevel image description consisting of proposed pixel-level spatial and object-level semantic features is developed for OBIC of PolSAR images. At the image pixel level, based on th combination of polarimetric and morphological image descriptions, polarimetric morphological profiles are developed to describe pixel-level spatial features. At the image object level, based on the construction of object adjacent graph, an object-level semantic indicator is proposed, which takes into account the contextual neighborhood of image objects. Finally, the proposed pixel-level spatial and object-level semantic features are integrated and incorporated in an OBIC scheme for the PolSAR image classification. Two fully polarized datasets acquired by ESAR and uninhabited airborne vehicle synthetic aperture radar (UAVSAR), respectively, are adopted to evaluate the effectiveness of the proposed method. The experimental results validate that the comprehensive utilization of both pixel-level and object-level features can effectively improve the OBIC accuracy of PolSAR images.

Object-Based Classification of Greenhouses Using Sentinel-2 MSI and SPOT-7 Images: A Case Study from Anamur (Mersin), Turkey

Accurate and reliable greenhouse mapping using remotely sensed data and image classification methods has a significant role since it can comprehensively improve the urban and rural planning, and sustainable natural resource and agricultural management. This research is mainly focused on the determination of greenhouses from SPOT-7 and Sentinel-2 MultiSpectral Instrument (MSI) images by using an object-based image classification method with three different classifiers which are k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) in the selected test region. First, the image acquired by using multi-resolution segmentation. Second, spectral features, textural features, and remote sensing indices were obtained for each image object. Third, different classifiers were employed to classify greenhouses. Then, classification accuracy assessment analysis was conducted to test the agreement between the classified data and field collected data using the confusion matrix. The results highlighted that the KNN and RF classifier have a slightly higher overall accuracy (OA) and Kappa statistics for SPOT-7 image with the 91.43% and 0.88. Furthermore, the KNN classifier for Sentinel-2 MSI image has the highest OA and Kappa statistics of 88.38% and 0.83. The achieved results underlined the potential of Sentinel-2 MSI and SPOT-7 data for object-based greenhouse mapping using different machine learning classifiers in the Mediterranean Region.