Abstract
Classification and change analysis based on high spatial resolution imagery are highly desirable for urban landscapes. However, methods with both high accuracy and efficiency are lacking. Here, we present a novel approach that integrates backdating and transfer learning under an object-based framework. Backdating is used to optimize the target area to be classified, and transfer learning is used to select training samples for classification. We further compare the new approach with that of using backdating or transfer learning alone. We found: (1) The integrated new approach had higher overall accuracy for both classifications (85.33%) and change analysis (88.67%), which were 2.0% and 4.0% higher than that of backdating, and 9.3% and 9.0% higher than that of transfer learning, respectively. (2) Compared to approaches using backdating alone, the use of transfer learning in the new approach allows automatic sample selection for supervised classification, and thereby greatly improves the efficiency of classification, and also reduces the subjectiveness of sample selection. (3) Compared to approaches using transfer learning alone, the use of backdating in the new approach allows the classification focusing on the changed areas, only 16.4% of the entire study area, and therefore greatly improves the efficiency and largely avoid the false change. In addition, the use of a reference map for classification can improve accuracy. This new approach would be particularly useful for large area classification and change analysis.
Highlights
Land use/land cover (LULC) change is one of the major drivers of biodiversity loss, air pollution, urban heat island (UHI), water shortage and pollution, and ecosystem degradation from local to regional, and even global scales [1,2]
The transfer learning approach can automatically choose a large number of training samples based on the existing land cover map, which promotes the efficiency of machine learning classification [10]
Compared with the classification accuracy of the medium resolution images, which are usually higher than 85% [9,10,12,19], backdating (83.3%) and transfer learning (76%) achieved relatively lower accuracy when applied to high resolution images
Summary
Land use/land cover (LULC) change is one of the major drivers of biodiversity loss, air pollution, urban heat island (UHI), water shortage and pollution, and ecosystem degradation from local to regional, and even global scales [1,2]. While most studies have used medium or coarser resolution data such as Landsat, MODIS, AVHRR, and SPOT-VEGETATION for LULC change analysis [4,5,6], high spatial resolution imagery has been increasingly used to quantify the fine-scale LULC change especially in urban landscapes with a wide availability of such data [7,8]. The backdating/updating and the transfer learning are two promising approaches that use the prior information of an existing land cover map for accurate and efficient classification. The backdating/updating approach conducts the classification in the changing area instead of the whole study area, which brings high efficiency and reduces “false changes” [9]. The transfer learning approach can automatically choose a large number of training samples based on the existing land cover map, which promotes the efficiency of machine learning classification [10]
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