Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria

Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based on segments was done by a nearest neighbour classifier. In the pixel-based classification, the spectral angle mapper was used to classify the images. The user accuracy for each class using object based classification were 98.31% for waterbody, 92.31% for vegetation, 86.67% for bare soil and 90.57% for Built up while the user accuracy for the pixel based classification were 98.28% for waterbody, 84.06% for Vegetation 86.36% and 79.41% for Built up. These classification techniques were subjected to accuracy assessment and the overall accuracy of the Object based classification was 94.47%, while that of Pixel based classification yielded 86.64%. The result of classification and accuracy assessment show that the object-based approach gave more accurate and satisfying results

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Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data

10.29007/jvz3 ◽

2018 ◽

Cited By ~ 2

Author(s):

Mohamed Mostafa Mohamed ◽

Samy Elmahdy

Keyword(s):

Land Use ◽

Land Cover ◽

Accuracy Assessment ◽

Low Cost ◽

Confusion Matrix ◽

Land Development ◽

Remote Sensing Data ◽

Land Use Land Cover ◽

Spectral Angle Mapper ◽

Fast Pace

Dubai is a rapidly urbanizing emirate with land development succeeding at a fast pace. The present study aims to develop a low-cost classifier based on the spectral angle mapper (SAM) and image difference (ID) algorithms. The proposed approach was developed in order to improve Land use/ Land cover (LULC) classification maps for the purpose of monitoring and analysing LULC change during the period from 2000 to 2015 for the Emirate of Dubai. The approach starts by collecting 320 training samples from high resolution images such as QuickBird with a spatial resolution of 60 cm followed by applying a 3×3 spatial convulsion filter, majority/ minority analysis, sieving classes and clump map of the produced LULC maps. After that, the accuracy of the maps were assigned using confusion matrix. The accuracy assessment demonstrated that the targeted 2000, 2005,2010 and 2015 LULC maps have 88.125%, 89.069%, 90.122% and 96.096% accuracy, respectively. The results exhibited that the built-up areas increased by 233.72 km2 (5.81%) from 2000 to 2005 and keeps to increase even up and till the present time. The results also showed that the changes in the periods 2000-2005 and 2010-2015 confirmed that net vegetation area loses were more obvious from 2005 to 2005 than from 2010 to 2015, reducing from 47.618 km2 to 40,820 km2, respectively. This study is of great help to urban planners and decision makers.

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Object Segmentation on UAV Photo Data to Support the Provision of Rural Area Spatial Information

Forum Geografi ◽

10.23917/forgeo.v29i1.792 ◽

2015 ◽

Vol 29 (1) ◽

Cited By ~ 1

Author(s):

Nurwita Mustika Sari ◽

Dony Kushardono

Keyword(s):

Rural Area ◽

Rural Areas ◽

Spatial Information ◽

Remote Sensing Data ◽

Image Data ◽

Aerial Photographs ◽

Segmentation Method ◽

Object Based ◽

Aerial Vehicle ◽

Uav Image

The use of Unmanned Aerial Vehicle (UAV) to take aerial photographs is increasing in recent years. Photo data taken by UAV become one of reliable detailed-scale remote sensing data sources. The capability to obtain cloud-free images and the flexibility of time are some of the advantages of UAV photo data compared to satellite images with optical sensor. Displayed area at the data shows the objects clearly. Rural area has certain characteristics in its land cover namely ricefield. To delineate the area correctly there is an object-based image analysis methods (OBIA) that could be applied. In this study, proposed a novel method to execute the separation of objects that exist in the data with segmentation method. The result shows an effective segmentation method to separate different objects in rural areas recorded on UAV image data. The accuracy obtained is 90.47% after optimization process. This segmentation can be a valid basis to support the provision of spatial information in rural area.

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Assessment of Coral Reef Life-Form Classification Scheme using Multiresolution Images on Parang Island, Indonesia

Geosfera Indonesia ◽

10.19184/geosi.v6i3.27592 ◽

2021 ◽

Vol 6 (3) ◽

pp. 377

Author(s):

Wahyu Lazuardi ◽

Pramaditya Wicaksono

Keyword(s):

Coral Reef ◽

Life Form ◽

Classification Scheme ◽

Spatial Information ◽

Satellite Image ◽

Creative Commons ◽

Central Java ◽

Object Based ◽

Efficient Data ◽

Sentinel 2A

Spatial information on the varying composition of coral reefs is beneficial for the management and preservation of natural resources in coastal areas. Its availability is inseparable from environmental management goals; however, it can also be used as a means of supporting tourism activities and predicting the emergence of certain living species. A satellite image is one of the effective and efficient data sources that provide spatial information on coral reef variations. This study aimed to evaluate the classification scheme of coral reef life-form using images with different spatial resolutions on Parang Island, Karimunjawa Islands, Central Java. These images were from PlanetScope (3m), PlanetScope resampling (6m), and Sentinel-2A MSI (10m), whose spatial resolutions functioned as the base for building the 3m, 6m, and 10m classification schemes producing 12, 11, and 9 classes, respectively. As for the classification method, it integrated both object-based and pixel-based approaches. The results showed that the highest overall accuracy (60%) was obtained using Sentinel-2A MSI image (10m), followed by PlanetScope (3m) with 48% accuracy, and PlanetScope resampling (6m) with 40% accuracy. This finding indicates that multiresolution images can be used to produce complex coral reef life-form maps with different levels of information details. Keywords: Coral reef; Life-form; Planetscope; Spatial resolution; Classification scheme Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Accuracy Assessment of Satellite Image Classification Depending on Training Sample

Austrian Journal of Statistics ◽

10.17713/ajs.v28i4.522 ◽

2016 ◽

Vol 28 (4) ◽

Cited By ~ 3

Author(s):

Georg Ruppert ◽

Mushtaq Hussain ◽

Heimo Müller

Keyword(s):

Classification Accuracy ◽

Accuracy Assessment ◽

Satellite Image ◽

Remote Sensing Data ◽

Ground Truth ◽

Training Sample ◽

Training Set ◽

Sampling Plans ◽

Training Sets

The paper presents a method of predicting classification accuracy of remote sensing data by means of training set analysis. Various sampling plans were applied to satellite image and its complete ground truth to derive different training sets. The quality of these training sets was determined by quantifying the similarity of the training set distributions to the ones of the entire satellite image. Each training set was then used to learn a classifier.The paper shows how the accuracy of classifications that were carried out using these classifiers depends upon the quality of the corresponding training sets.

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Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-7-31-2014 ◽

2014 ◽

Vol II-7 ◽

pp. 31-37 ◽

Cited By ~ 21

Author(s):

T. Kavzoglu ◽

M. Yildiz

Keyword(s):

Image Analysis ◽

Accuracy Assessment ◽

Data Sets ◽

Image Objects ◽

Object Based Image Analysis ◽

Local Variance ◽

Object Based ◽

Segmented Images ◽

Nearest Neighbour Classifier ◽

Segmentation Parameters

Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.

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Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

Remote Sensing ◽

10.3390/rs13040830 ◽

2021 ◽

Vol 13 (4) ◽

pp. 830

Author(s):

Adam R. Benjamin ◽

Amr Abd-Elrahman ◽

Lyn A. Gettys ◽

Hartwig H. Hochmair ◽

Kyle Thayer

Keyword(s):

Image Analysis ◽

Aquatic Vegetation ◽

Accuracy Assessment ◽

Unmanned Aerial Systems ◽

Temporal Area ◽

Object Based Image Analysis ◽

Object Based ◽

Significant Difference ◽

Additional Processing ◽

Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

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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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Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

Remote Sensing ◽

10.3390/rs12111772 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1772

Author(s):

Brian Alan Johnson ◽

Lei Ma

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Image Segmentation ◽

Online Survey ◽

Accuracy Assessment ◽

Learning Approaches ◽

Special Issue ◽

Object Based Image Analysis ◽

Object Based ◽

Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Remote Sensing ◽

10.3390/rs12010096 ◽

2019 ◽

Vol 12 (1) ◽

pp. 96 ◽

Cited By ~ 6

Author(s):

James Brinkhoff ◽

Justin Vardanega ◽

Andrew J. Robson

Keyword(s):

Land Cover ◽

Large Scale ◽

Satellite Image ◽

Resource Planning ◽

Support Vector ◽

Perennial Crop ◽

Perennial Crops ◽

Object Based ◽

Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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