A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier

Ali, Muhammad Zeeshan; Qazi, Waqas A.; Aslam, Nasir

doi:10.1016/j.ejrs.2018.03.003

Cited by 43 publications

(29 citation statements)

References 30 publications

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“…Unsupervised classification only depends on the distribution of pixel gray value in spectral feature space, which makes it difficult to achieve accurate matching between pixel clusters and ground classes. Because of its simple calculation and strong stability, maximum likelihood method is still widely used in supervised classification algorithm [42]. The maximum likelihood method is an image classification method that combines the distances of samples to the centers of known classes and spectral distribution features.…”

Section: Plos Onementioning

confidence: 99%

Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data

et al. 2020

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Mastering the evolution of urban land cover is important for urban management and planning. In this paper, a method for analyzing land cover evolution within urban built-up areas based on nighttime light data and Landsat data is proposed. The method solves the problem of inaccurate descriptions of urban built-up area boundaries from the use of single-source diurnal or nocturnal remote sensing data and was able to achieve an effective analysis of land cover evolution within built-up areas. Four main procedures are involved: (1) The neighborhood extremum method and maximum likelihood method are used to extract nighttime light data and the urban built-up area boundaries from the Landsat data, respectively; (2) multisource urban boundaries are obtained using boundary pixel fusion of the nighttime light data and Landsat urban built-up area boundaries; (3) the maximum likelihood method is used to classify Landsat data within multisource urban boundaries into land cover classes, such as impervious surface, vegetation and water, and to calculate landscape indexes, such as overall landscape trends, degree of fragmentation and degree of aggregation; (4) the changes in the multisource urban boundaries and landscape indexes were obtained using the abovementioned methods, which were supported by multitemporal nighttime light data and Landsat data, to model the urban land cover evolution. Using the cities of Shenyang, Changchun and Harbin in northeastern China as experimental areas, the multitemporal landscape index showed that the integration and aggregation of land cover in the urban areas had an increasing trend, the natural environment of Shenyang and Harbin was improving, while Changchun laid more emphasis on the construction of artificial facilities. At the same time, the method proposed in this paper to extract built-up areas from multi-source city data showed that the user accuracy, production accuracy, overall accuracy and Kappa coefficient are at least 3%, 1%, 1% and 0.04 higher than the single-source data method.

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Section: Plos Onementioning

confidence: 99%

Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data

et al. 2020

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“…These data were generated from four sources: (1) field-collected land cover survey locations, (2) reference locations derived from field photos, (3) reference locations derived from very high-resolution Bing aerial and Google Earth imagery, and (4) expert knowledge and direct interpretation of reference locations of clear imagery features (for example, water). The use of higher resolution imagery accessible via public portals such as Google Earth for identification of reference data points is an established technique [38]. For each land cover class, the reference data points were allocated on an alternating basis as training or independent validation locations, which created two equal-sized datasets providing 352 locations for both the training and validation datasets (704 in total) distributed across all land cover classes (built up 7.1%, bare 14.2%, water 10.8%, dry grassland 14.2%, alpine grassland 14.2%, steppe 14.2%, bushes 11.1%, and agriculture 14.2%).…”

Section: Land Cover Classificationmentioning

confidence: 99%

“…Optical and Synthetic Aperture Radar (SAR) remote sensing datasets characterise target features in different ways, but deliver complementary information that, if used in combination, generally leads to an increase in land cover mapping accuracy [37]. Multispectral optical remote sensing imagery delivers rich spectral information about the scattered energy (visible and infrared) from the target surface, which enables the discrimination of different land cover classes on the basis of spectral variations in the specific features in question [38]. In contrast, SAR characterises the physical structure of target features, with the differing spatial structure of these features scattering the SAR signal at differing levels of intensity and amplitude.…”

Section: Introductionmentioning

confidence: 99%

“…The ground surface texture strongly influences SAR backscatter [39], and the varying characteristics of SAR backscatter can be used to derive information about that target feature. By using SAR-based texture (structural) and optical (spectral) information synergistically to exploit the different physical principles, which they characterise, improved classifications of land cover have been observed [37,40], compared to where either optical or SAR data have been used independently [38]. This has been illustrated for examples such as mapping of winter wheat in China [35], maize, soybeans, and sunflowers in Ukraine [41,42], forestry studies [43][44][45][46], and mapping land cover [47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

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On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping

Marston

Giraudoux

2018

Remote Sensing

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(1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery.

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“…27 In the latter, pixels are grouped based on the reflectance properties of pixels, and the created groups are called "clusters;" the former is performed by selecting representative samples for each class in the image, and the objective classification is based on spectral signatures defined by the user. 28 The most commonly used supervised classifications include the support vector machine, 29 maximum likelihood, 30 decision tree, 31 random forest, 32 and neural network classification 33 techniques, and the most common unsupervised classification methods include the K-means clustering 34 and ISODATA classification 35 approaches. In addition to the band intensity, these methods can use more information, such as textural and multiband data.…”

Section: Introductionmentioning

confidence: 99%

Using the modified two-mode method to identify surface water in Gaofen-1 images

Zhang

et al. 2018

J. Appl. Rem. Sens.

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The rapid, accurate, and automated extraction of surface water is highly important for conducting reliable and necessary surface water monitoring endeavors. Classification methods commonly exhibit high precision but also have a low degree of automation or narrow scope of application; commonly used water index methods are highly efficient, but they easily mistake other targets with similar spectral characteristics for surface water. Simultaneously achieving precision, efficiency, and automation within a single method is a challenge. To address these problems, we simplify the normalized different water index (NDWI) to a band ratio index and traverse the neighborhood of the extreme in the histogram to determine two peaks and one trough between the peaks in the two-mode method, and we then compare the middle value of the two peaks with the value of the trough to confirm the threshold of the surface water. We use the modified two-mode method to extract Poyang Lake from four Chinese Gaofen (GF)-1 remote sensing images corresponding to different seasons, and then compare the results with those obtained by the NDWI index and the maximization of interclass variance (OTSU) method. The comparison shows that our method has higher and more stable accuracy, especially during the drought period for Poyang Lake. However, polluted water, narrow rivers, bridges, and residential areas along the lake are sometimes mistakenly extracted. Finally, the advantages and prospects of the proposed method are discussed.

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A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier

Cited by 43 publications

References 30 publications

Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data

Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data

On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping

Using the modified two-mode method to identify surface water in Gaofen-1 images

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