Dual-polarimetric C-band SAR data for land use/land cover classification by incorporating textural information

Mishra, Varun Narayan; Prasad, Rajendra; Kumar, Pradeep; Gupta, Dileep Kumar; Srivastava, Prashant K.

doi:10.1007/s12665-016-6341-7

Cited by 56 publications

(26 citation statements)

References 34 publications

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“…For all four single features, overall accuracies were lower than 80% except for the value obtained using T. Although the overall accuracy of T was higher than 85%, the corresponding F1 measures of GRA and BIS were lower than 85%, which indicates that four single features could not meet the requirements of urban land cover classification. Consistent with previous studies, classification accuracy of less than 85% when using single polarization image [ 46 ] and using only coherence features [ 92 ]. The classification accuracy of the backscatter intensity features in this study was higher than previous studies using only single-date dual-polarimetric SAR data [ 93 ], mainly due to the use of multi-temporal SAR data.…”

Section: Resultssupporting

confidence: 90%

“…As shown in Table 4 , the classification accuracy using T was highest, with an overall accuracy of 89.08% (kappa coefficient = 0.8621), followed by VV + VH and C2. Compared with the previous studies [ 46 , 90 ], the classification accuracy of the texture features in this study was higher mainly because each individual texture feature was calculated using their corresponding best window. Although C1 had the worst performance (with an overall accuracy of 58.45% and a kappa coefficient of 0.4734) of the four single features, it could distinguish urban impervious surfaces (BIS and DIS) well.…”

Section: Resultsmentioning

confidence: 72%

“…Since SAR data can provide abundant texture information, many previous studies have extracted texture features from SAR data for LULC information extraction. Some previous studies used the combination of texture and backscatter intensity features to map urban land cover and found that texture features were important information that can improve classification accuracy [ 46 , 47 ]. In addition to backscatter intensity and texture features, some researchers also extracted coherence and color features from SAR data for urban land cover classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

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Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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“…This study selected training and testing samples through field research and defined region of interest (ROI) files by ENVI 5.0 software for different land cover categories using ground true data [33]. To ensure the accuracy of the samples, the training and testing samples were selected and validated as follows: (1) the sample type was determined through the first field research and an analysis of land use, (2) the sample type and regions were confirmed further with HJ-1/CCD images and high-resolution images from Google Earth, and (3) the sample types and region accuracy were further confirmed through a second field survey with the same research route.…”

Section: Samples Selectionmentioning

confidence: 99%

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

Zhao

Zhang

Pan

2017

Mathematical Problems in Engineering

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A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of -means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

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“…It has an ability to classify large datasets since it considers dimensionality reduction procedures and also handles missing and outlier values [33]. This classifier's performance is comparable to that of other classifiers like Support Vector Machine and is better than many other classifiers like Maximum Likelihood Classifier [34] and Artificial Neural Network [35,36].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

Malhi

Asundi

Srivastava

et al. 2020

IJGI

Self Cite

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Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

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Dual-polarimetric C-band SAR data for land use/land cover classification by incorporating textural information

Cited by 56 publications

References 34 publications

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

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