Abstract
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.
Highlights
Over the past few decades, the world has undergone an unprecedented process of urbanization and it is estimated that by 2030, 60% of the global population will live in cities [1]
The objective of this study was to evaluate the ability and contribution of using backscatter intensity, texture, coherence and color features extracted from Sentinel-1A data for urban land cover classification and to compare different multi-sensor land cover mapping methods to improve classification accuracy
In order to assess the ability and contribution of using backscatter intensity, texture, coherence and color features extracted from Sentinel-1A data for urban land cover classification and to compare different multi-sensor land cover mapping methods to improve classification accuracy, the following combinations were considered (Table 3)
Summary
Over the past few decades, the world has undergone an unprecedented process of urbanization and it is estimated that by 2030, 60% of the global population will live in cities [1]. The rapid population growth has caused unhealthy housing, air pollution, traffic congestion, food security and other issues in urban areas. Accurate and timely collection of reliable urban land use and land cover (LULC) information is the key to addressing these issues and achieving sustainable urban development, which is important for urban planners and decision-makers. Due to its characteristics of frequent and large area detection, remote sensing technology has become an important means to obtain the information of LULC quickly, and has made great contributions to monitoring the process of dynamic urbanization.
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