A Land Use Classification Model Based on Conditional Random Fields and Attention Mechanism Convolutional Networks

Abstract

Land use is used to reflect the expression of human activities in space, and land use classification is a way to obtain accurate land use information. Obtaining high-precision land use classification from remote sensing images remains a significant challenge. Traditional machine learning methods and image semantic segmentation models are unable to make full use of the spatial and contextual information of images. This results in land use classification that does not meet high-precision requirements. In order to improve the accuracy of land use classification, we propose a land use classification model, called DADNet-CRFs, that integrates an attention mechanism and conditional random fields (CRFs). The model is divided into two modules: the Dual Attention Dense Network (DADNet) and CRFs. First, the convolution method in the UNet network is modified to Dense Convolution, and the band-hole pyramid pooling module, spatial location attention mechanism module, and channel attention mechanism module are fused at appropriate locations in the network, which together form DADNet. Second, the DADNet segmentation results are used as a priori conditions to guide the training of CRFs. The model is tested with the GID dataset, and the results show that the overall accuracy of land use classification obtained with this model is 7.36% and 1.61% higher than FCN-8s and BiSeNet in classification accuracy, 11.95% and 1.81% higher in MIoU accuracy, and with a 9.35% and 2.07% higher kappa coefficient, respectively. The proposed DADNet-CRFs model can fully use the spatial and contextual semantic information of high-resolution remote sensing images, and it effectively improves the accuracy of land use classification. The model can serve as a highly accurate automatic classification tool for land use classification and mapping high-resolution images.

Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods

“…The remote sensing data sources used include MODIS [13,14], the Landsat series [15][16][17][18], and DMSP/OLS nighttime light data [19][20][21][22], but the spatial resolution of these remote sensing products is relatively low. High-resolution remote sensing data sources include QuickBird, IKONOS, and Gaofen2 [23][24][25]; these products are very expensive, and Sentinel-2, which is free and has high resolution, has therefore become the data source of choice for many studies [26]. By processing remote-sensing images and extracting remote-sensing information and features, the boundaries of built-up urban areas can be better extracted [10].…”

Semantic segmentation of remote sensing images based on dilated convolution and spatial-channel attention mechanism

“…In urban planning, it has been utilized for road detection, 5 building change detection, 6 illegal building detection, 7 3D scene reconstruction. 8,9 In the field of land cover mapping, 10 it has been used to detect forest land, agricultural land, bare land, irrigated land, and dry land. As the classification of ground objects becomes increasingly refined, the demand for more advanced and sophisticated classification and segmentation algorithms has significantly increased.…”

“…Due to its advanced classification capabilities, semantic segmentation has found wide application in many fields, such as landslide monitoring, 1 , 2 plant disease monitoring, 3 and water quality monitoring, 4 in the field of disaster monitoring. In urban planning, it has been utilized for road detection, 5 building change detection, 6 illegal building detection, 7 3D scene reconstruction 8 , 9 . In the field of land cover mapping, 10 it has been used to detect forest land, agricultural land, bare land, irrigated land, and dry land.…”

Using Multiple Sources of Data and “Voting Mechanisms” for Urban Land-Use Mapping

“…However, the heterogeneity of land-use features in remote-sensing images and the confusion of image elements usually lead to a decrease in urban land-use classification accuracy. For example, the spectral characteristics of commercial and residential areas in remote-sensing images are very similar, and it is Land 2022, 11, 2209 2 of 18 difficult to further improve the accuracy of land-use classification while relying only on remote-sensing images [14,15].…”