Fusion Methods and Multi-Classifiers for Improving Land Cover Estimation By Remote Sensing Analysis

2021 ◽  
Author(s):  
Hayder Dibs ◽  
Hashim Ali Hasab ◽  
Ammar Shaker Mahmoud ◽  
Nadhir Al-Ansari
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Confusion Matrix ◽  
Kappa Coefficient ◽  
Accurate Estimation ◽  
Support Vector ◽  
Landsat 8 ◽  
Neural Net ◽  
Spectral Angle Mapper

Abstract Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of land-use and land-cover (LU/LC) is a very difficult task to perform. Image fusion plays a big role to map the LU/LC. Therefore, This study aims to find out a refining method for the LU/LC estimating by adopting these steps; (1) apply a three pan-sharpening fusion approaches to combine panchromatic (PAN) imagery has high spatial resolution with multispectral (MS) imagery has low spatial resolution, (2) employing five pixel-based classifier approaches on MS and fused images; artificial neural net (ANN), support vector machine (SVM), parallelepiped (PP), Mahalanobis distance (Mah) and spectral angle mapper (SAM), (3) Make a statistical comparison between classification results. The Landsat-8 image was adopted for this research. There are twenty LU/LC thematic maps were created in this study. A suitable and reliable LU/LC method was presented based on the obtained results. The validations of the results were performed by adopting a confusion matrix. A comparison made between the classification results of MS and all fused images levels. It proved that mapping the LU/LC produced by Gram-Schmidt Pan-sharpening (GS) and classified by SVM method has the most accurate result among all other MS and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the SAM algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

scholarly journals Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis

2021 ◽  
Author(s):  
Hayder Dibs ◽  
Hashim Ali Hasab ◽  
Ammar Shaker Mahmoud ◽  
Nadhir Al-Ansari
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Multispectral Imagery ◽  
Spectral Angle Mapper ◽  
Spectral Angle

AbstractAdopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

scholarly journals A COMPARATIVE ASSESSMENT OF EFFICACY OF SUPER RESOLVED AIRBORNE HYPERSPECTRAL OUTPUTS IN URBAN MATERIAL AND LAND COVER INFORMATION EXTRACTION

2018 ◽  
Vol XLII-5 ◽  
pp. 653-658 ◽  
Author(s):  
K. Mishra ◽  
A. Siddiqui ◽  
V. Kumar
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Urban Areas ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Spatial Information ◽  
Confusion Matrix ◽  
Spectral Characteristics ◽  
Support Vector ◽  
Spectral Angle Mapper

<p><strong>Abstract.</strong> Urban areas despite being heterogeneous in nature are characterized as mixed pixels in medium to coarse resolution imagery which renders their mapping as highly inaccurate. A detailed classification of urban areas therefore needs both high spatial and spectral resolution marking the essentiality of different satellite data. Hyperspectral sensors with more than 200 contiguous bands over a narrow bandwidth of 1&amp;ndash;10<span class="thinspace"></span>nm can distinguish identical land use classes. However, such sensors possess low spatial resolution. As the exchange of rich spectral and spatial information is difficult at hardware level resolution enhancement techniques like super resolution (SR) hold the key. SR preserves the spectral characteristics and enables feature visualization at a higher spatial scale. Two SR algorithms: Anchored Neighbourhood Regression (ANR) and Sparse Regression and Natural Prior (SRP) have been executed on an airborne hyperspectral scene of Advanced Visible/Near Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for the mixed environment centred on Kankaria Lake in the city of Ahmedabad thereby bringing down the spatial resolution from 8.1<span class="thinspace"></span>m to 4.05<span class="thinspace"></span>m. The generated super resolved outputs have been then used to map ten urban material and land cover classes identified in the study area using supervised Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods. Visual comparison and accuracy assessment on the basis of confusion matrix and Pearson’s Kappa coefficient revealed that SRP super-resolved output classified using radial basis function (RBF) kernel based SVM is the best outcome thereby highlighting the superiority of SR over simple scaling up and resampling approaches.</p>

scholarly journals OBJECT-BASED IMAGE ANALYSIS OF DIFFERENT SPATIAL RESOLUTION SATELLITE IMAGERIES IN URBAN AND SUBURBAN ENVIRONMENT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 105-112
Author(s):  
I. Kotaridis ◽  
M. Lazaridou
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Land Cover ◽  
Spatial Resolution ◽  
Support Vector ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Very High Spatial Resolution ◽  
Sentinel 2A

Abstract. Monitoring urban and suburban land cover has become a particularly researched investigation field in remote sensing community, since there is a large amount of professionals interested in gathering useful information, regarding urban sprawl and its side effects in natural vegetation, urban parks and water bodies. This paper focuses on studying the implementation of an object-based image analysis methodological framework, in Orfeo ToolBox. Moderate, high and very high spatial resolution satellite images were utilized in order to generate thematic land cover maps of the study area located in Thessaloniki, Greece. Taking into consideration that there is not a relevant research in literature concerning the selection of segmentation parameters values, the optimal values are presented for MeanShift segmentation algorithm. Classifications were conducted with the use of Support Vector Machines algorithm and the final outputs are presented, accompanied by the evaluation of accuracy assessments which is a mandatory step in every remote sensing project. The analysis showed that OBIA, in this case, works well with Landsat-8 and QuickBird data and exceptionally well with Sentinel-2A data with over 90% overall accuracy. Critical considerations on the aforementioned are also included.

scholarly journals Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

2020 ◽  
Vol 12 (1) ◽  
pp. 177 ◽  
Author(s):  
Mahendra Pal ◽  
Thorkild Rasmussen ◽  
Alok Porwal
Keyword(s):  
Remote Sensing ◽  
Kappa Coefficient ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Spectral Angle Mapper ◽  
Lithological Mapping ◽  
Accuracy Index ◽  
Hyperspectral Remote Sensing Images

Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

scholarly journals Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors

2020 ◽  
Vol 12 (8) ◽  
pp. 1263 ◽  
Author(s):  
Yingfei Xiong ◽  
Shanxin Guo ◽  
Jinsong Chen ◽  
Xinping Deng ◽  
Luyi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
The Cross ◽  
Model Training

Detailed and accurate information on the spatial variation of land cover and land use is a critical component of local ecology and environmental research. For these tasks, high spatial resolution images are required. Considering the trade-off between high spatial and high temporal resolution in remote sensing images, many learning-based models (e.g., Convolutional neural network, sparse coding, Bayesian network) have been established to improve the spatial resolution of coarse images in both the computer vision and remote sensing fields. However, data for training and testing in these learning-based methods are usually limited to a certain location and specific sensor, resulting in the limited ability to generalize the model across locations and sensors. Recently, generative adversarial nets (GANs), a new learning model from the deep learning field, show many advantages for capturing high-dimensional nonlinear features over large samples. In this study, we test whether the GAN method can improve the generalization ability across locations and sensors with some modification to accomplish the idea “training once, apply to everywhere and different sensors” for remote sensing images. This work is based on super-resolution generative adversarial nets (SRGANs), where we modify the loss function and the structure of the network of SRGANs and propose the improved SRGAN (ISRGAN), which makes model training more stable and enhances the generalization ability across locations and sensors. In the experiment, the training and testing data were collected from two sensors (Landsat 8 OLI and Chinese GF 1) from different locations (Guangdong and Xinjiang in China). For the cross-location test, the model was trained in Guangdong with the Chinese GF 1 (8 m) data to be tested with the GF 1 data in Xinjiang. For the cross-sensor test, the same model training in Guangdong with GF 1 was tested in Landsat 8 OLI images in Xinjiang. The proposed method was compared with the neighbor-embedding (NE) method, the sparse representation method (SCSR), and the SRGAN. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were chosen for the quantitive assessment. The results showed that the ISRGAN is superior to the NE (PSNR: 30.999, SSIM: 0.944) and SCSR (PSNR: 29.423, SSIM: 0.876) methods, and the SRGAN (PSNR: 31.378, SSIM: 0.952), with the PSNR = 35.816 and SSIM = 0.988 in the cross-location test. A similar result was seen in the cross-sensor test. The ISRGAN had the best result (PSNR: 38.092, SSIM: 0.988) compared to the NE (PSNR: 35.000, SSIM: 0.982) and SCSR (PSNR: 33.639, SSIM: 0.965) methods, and the SRGAN (PSNR: 32.820, SSIM: 0.949). Meanwhile, we also tested the accuracy improvement for land cover classification before and after super-resolution by the ISRGAN. The results show that the accuracy of land cover classification after super-resolution was significantly improved, in particular, the impervious surface class (the road and buildings with high-resolution texture) improved by 15%.

crops planting area identification and analysis based on multi-source high resolution remote sensing data

2020 ◽  
Author(s):  
Lei Wang ◽  
Haoran Sun ◽  
Wenjun Li ◽  
Liang Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Classification Accuracy ◽  
Kappa Coefficient ◽  
Optical Data ◽  
Accurate Estimation ◽  
Support Vector ◽  
Random Forest Method ◽  
Multi Temporal ◽  
Sar Data

&lt;p&gt;Crop planting structure is of great significance to the quantitative management of agricultural water and the accurate estimation of crop yield. With the increasing spatial and temporal resolution of remote sensing optical and SAR(Synthetic Aperture Radar) images, &amp;#160;efficient crop mapping in large area becomes possible and the accuracy is improved. In this study, Qingyijiang Irrigation District in southwest of China is selected for crop identification methods comparison, which has heterogeneous terrain and complex crop structure . Multi-temporal optical (Sentinel-2) and SAR (Sentinel-1) data were used to calculate NDVI and backscattering coefficient as the main classification indexes. The multi-spectral and SAR data showed significant change in different stages of the whole crop growth period and varied with different crop types. Spatial distribution and texture analysis was also made. Classification using different combinations of indexes were performed using neural network, support vector machine and random forest method. The results showed that, the use of multi-temporal optical data and SAR data in the key growing periods of main crops can both provide satisfactory classification accuracy. The overall classification accuracy was greater than 82% and Kappa coefficient was greater than 0.8. SAR data has high accuracy and much potential in rice identification. However optical data had more accuracy in upland crops classification. In addition, the classification accuracy can be effectively improved by combination of classification indexes from optical and SAR data, the overall accuracy was up to 91.47%. The random forest method was superior to the other two methods in terms of the overall accuracy and the kappa coefficient.&lt;/p&gt;

scholarly journals Impact of the Spatial Resolution of Satellite’s Data for Mapping Land use Land Cover in Karmala Tehsil, Solapur, India

2021 ◽  
pp. 66-75
Author(s):  
Sanket Kolambe ◽  
Jeet Raj ◽  
Krishna Loahkare ◽  
Shital Mane ◽  
Vikrant Nikam
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Classification Accuracy ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Wide Field ◽  
Barren Land ◽  
Liss Iii ◽  
Sentinel 2A

Land use and land cover (LULC) classification mapping is important for evaluating, monitoring, protecting and planning for land resources. A key factor in extracting desired information from satellite images is choosing the right the spatial resolution. The scale of a pixel on the ground is known as spatial resolution. A pixel is the smallest ‘dot' that makes up an optical satellite image which defines the level of detail as in image. In this paper estimation of the areal extent of water, built up, barren land, vegetation land and fallow land classes with its classification accuracy were reviewed particularly for January 2013 and November 2016 in Karmala tehsil of Solapur district, India. LULC is implied by different spatial resolution images of Advanced Wide Field Sensor (AWiFS), Linear Imaging Self Scanning Sensor (LISS-III), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A imageries in QGIS environment while the classification was carried out using the maximum likelihood algorithm (MLA). The classified maps obtained from AWiFS and LISS-III sensors, as well as Sentinel-2A and Landsat-8 OLI data sets, were compared separately.  Spatial analysis depicts that the Kappa coefficient of Sentinal-2A, Landsat-8, LISS III and AWiFS was found 96.96%, 91.64%, 87.30% and 89.36%. Furthermore, overall accuracy of was found to be 99.07%, 94.49%, 89.84% and 94.08% respectively. The accuracy of the classified image with higher spatial resolution (Sentinal-2A) proved more informative than that of lower resolution (AWiFS) sensor. On the response, the finer spatial resolution of Sentinal-2A (10 m) delivered more precise details and enhanced LULC classification accuracy most reliably than the coarser spatial resolution of Landsat-8 (30m), LISS III (23m) and AWiFS (56m) image. A perusal of data revealed that the overall accuracy and Kappa coefficient was found proportionate to spatial resolution of satellite imageries. The higher resolution spatial data also greatly reduces the mixed-pixel problem. The study revealed that the spatial resolution plays an important role and affects classification details and accuracy of LULC level.

scholarly journals COMPARISON OF SUPERVISED CLASSIFICATION TECHNIQUES WITH ALOS PALSAR SENSOR FOR ROORKEE REGION OF UTTARAKHAND, INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 693-701 ◽  
Author(s):  
A. K. Shakya ◽  
A. Ramola ◽  
A. Kandwal ◽  
R. Prakash
Keyword(s):  
Supervised Classification ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Research Work ◽  
Weather Conditions ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Alos Palsar ◽  
Spectral Angle Mapper ◽  
Classification Techniques

<p><strong>Abstract.</strong> The Advanced Land Observing Satellite (ALOS) is developed by the Japanese Aerospace Exploration Agency (JAXA) which was launched in the year 2006 for the Earth observation and exploration purpose. The ALOS was carrying PRISM, AVNIR-2 and PALSAR sensors for this purpose. PALSAR is L-Band synthetic aperture radar (SAR). The PALSAR sensor is designed in a way that it can work in all weather conditions with a resolution of 10 meters. In this research work we have made an investigation on the accuracy obtained from the various supervised classification techniques. We have compared the accuracy obtained by classifying the ALOS PALSAR data of the Roorkee region of Uttarakhand, India. The training ROI’S (Region of Interest) are created manually with the assistance of ArcGIS Earth and for the testing purpose, we have used the Global positioning system (GPS) coordinates of the region. Supervised classification techniques included in this comparison are Parallelepiped classification (PC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper (SAM), Spectral information divergence (SID) and Support vector machine (SVM). Later, through the post classification confusion matrix accuracy assessment test is performed and the corresponding value of the kappa coefficient is obtained. In the result, we have concluded MDC as best in term of overall accuracy with 82.3634% and MLC with a kappa value of 0.7591. Finally, a peculiar relationship is developed in between classification accuracy and kappa coefficient.</p>

scholarly journals Comparison of Conventional Change Detection Methodologies Using High-Resolution Imagery to Find Forest Damage Caused by Typhoons

2020 ◽  
Vol 12 (19) ◽  
pp. 3242 ◽  
Author(s):  
Flavio Furukawa ◽  
Junko Morimoto ◽  
Nobuhiko Yoshimura ◽  
Masami Kaneko
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Forest Damage ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Spectral Angle Mapper ◽  
Filtering Method

The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used in different phases of disaster risk management because they can deliver information rapidly to the concerned parties. Although remote sensing technology is already available, an examination of appropriate methods based on the type of disaster is still missing. Our goal is to compare the suitability of three different conventional classification methods for fast and easy change detection analysis using high-spatial-resolution and high-temporal-resolution remote sensing imagery to identify areas with windthrow and landslides caused by typhoons. In August 2016, four typhoons hit Hokkaido, the northern island of Japan, creating large areas of windthrow and landslides. We compared the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method to identify windthrow and landslides in two different study areas in southwestern Hokkaido. These methodologies were evaluated using PlanetScope data with a resolution of 3 m/px and validated with reference data based on Worldview2 data with a very high resolution of 0.46 m/px. The results showed that all three methods, when applied to high-spatial-resolution imagery, can deliver sufficient results for windthrow and landslide detection. In particular, the SAM method performed better at windthrow detection, and the NDVI filtering method performed better at landslide detection.

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