- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples
摘要： Detailed information about built-up areas is valuable for mapping complex urban environments. Although a large number of classification algorithms for such areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore, we launched a unique investigation to provide a full test of the Operational Land Imager (OLI) imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we proposed a method based on the European Space Agency’s (ESA) 38-m global built-up area data of 2014, OpenStreetMap, and MOD13Q1-NDVI to achieve the rapid and automatic generation of a large number of sample points. Our aim was to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape, and texture as the input features, and support vector machine (SVM), random forest (RF), and AdaBoost as the classification algorithms. In feature learning, the convolutional neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. The experimental results show the following: (1) The approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers (e.g., RF) are comparable to that of CNN. Two-dimensional CNN and the 7-neighborhood RF have the highest classification accuracies at nearly 91%; (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (e.g., PanTex (texture-derived built-up presence index) and enhanced morphological building index (EMBI)) can help improve classification accuracy. The code and experimental results are available at https://github.com/zhangtao151820/CompareMethod.
关键词： classification,CNN,feature engineering,built-up area,Landsat 8-OLI,accuracy evaluation,feature learning
更新于2025-09-23 15:22:29
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Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery
摘要： In this study, an object-based automatic multi-index BUs extraction method was developed. First, several indices, including built-up areas extraction index (NBEIr-c), vegetation extraction index(NDVInir2-r) and water extraction index (NDWI b-nir1), were developed to obtain the BUs, vegetation and water maps, and then the fractional-order Darwinian particle swarm optimization (FODPSO) algorithm was employed to automatically segment the multi-index images and obtained BUs, water, vegetation and BS information. Finally, the extracted BUs results were optimized via an object-based analysis method and the results were compared with those of two other relevant indices, which confirmed the proposed indices had a higher accuracy and exhibited higher performance when separating the BS from the BUs.
关键词： Built-up areas,Object-based,Multi-index,Worldview-2,High spatial resolution images
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Precise Extraction of Built-Up Area Using Deep Features
摘要： Built-up area is one of the most important objects in remote sensing image analysis, therefore extracting built-up area automatically has attracted wide attention. Deep convolution neural network (CNN) was proposed to improve poor generalization ability of artificial features which had been adopted by traditional automatic extraction methods. In this paper, a more efficient CNN model is proposed to extract the deep features of remote sensing images, and then a graph model based on deep features is constructed to the full image for built-up area extraction. The experiments demonstrate that it has very good performance on the satellite remote sensing image data set.
关键词： Built-up area extraction,CNN,remote Sensing,cosine similarity,Graph cut
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Worldpop - Fusion of Earth and Big Data for Intraurban Population Mapping
摘要： High resolution estimates of human population distributions are very useful for large-scale or national scale analyses in many fields including epidemiology, healthcare, resource distribution, and development. Population densities have long been estimated using remote sensing data, particularly at large spatial scales. However, the accuracy of population density predictions can be very poor in cities, and this is particularly relevant in urban areas in sub-Saharan Africa. Here we map intra-urban population densities for select African cities by disaggregating census data using random forest techniques with remotely-sensed and geospatial data, including bespoke time-series intra-urban built-up data. We produce maps with up to 83% explained variance and find including built-up density layers in urban population models allows for clear improvements in prediction.
关键词： machine learning,population density,census,built-up,Urban areas,Africa
更新于2025-09-10 09:29:36