Automatic detection of photovoltaic facilities from Sentinel-2 observations by the enhanced U-Net method

Abstract

With the enactment of supportive government policies and the increasing maturity of solar photovoltaic (PV) technologies, solar PV energy has become the most cost-effective new energy resource worldwide. Geospatial information on existing solar PV power systems is necessary to manage and optimize the deployment of new PV facilities. In this study, we propose a new deep-learning network, named the enhanced U-Net (E-UNET), to detect PV facilities from Sentinel-2 multi-spectral remote sensing data. Our E-UNET features an enhanced encoderdecoder structure that can efficiently extract spectral and spatial features simultaneously by combining a multi-spectral three-dimensional convolution path and a multi-scale pooling block. We compare the performance of the E-UNET with other semantic segmentation deep-learning networks and a pixel-based random forest classifier. The experimental results show that the E-UNET performs better than the other methods. It achieves an overall accuracy, Matthews correlation coefficient, F1, kappa coefficient, and recall of 0.989, 0.862, 0.869, 0.934, and 0.875, respectively. The experimental results also indicate that the E-UNET accurately detects PV facilities from various complex environments with high accuracy in terms of PV integrity and details.

Segmentation and Connectivity Reconstruction of Urban Rivers from Sentinel-2 Multi-Spectral Imagery by the WaterSCNet Deep Learning Model

“…As shown in Figure 2, the WaterSCNet-s was improved from the Enhanced U-Net (E-UNet) architecture [17], which is capable of semantic segmentation of multi-spectral remote sensing imagery, for the task of urban river segmentation.…”

“…With the development of deep learning, various end-to-end structures based on a convolutional neural network (CNN) [14] have emerged, such as U-Net [15], U-Net++ [16], E-UNet [17], SegNet [18], and HRNet [19], which can automatically extract high-level information from satellite imagery without a tedious manual feature extraction process [20]. U-Net [15] is a U-shaped encoder-decoder segmentation network with skip connections.…”

“…The dense convolution blocks can bridge the semantic gap between encoder and decoder feature maps, and the deep supervision design can improve segmentation performance through model pruning [16,23]. E-UNet [17] enhances UNet's encoder-decoder structure by adding a multi-spectral three-dimensional convolution (MSD) path module [24] to capture nonlinear relationships between multispectral bands of adjacent pixels [25][26][27], and a multi-scale pooling (MSP) block [28] to perceive texture relationships and information at multiple spatial scales. Three-dimensional convolution is mainly used in the segmentation of hyper-spectral data [26,27], and is also widely used in multi-spectral and hyper-spectral image fusion [25] and multi-spectral time series image processing [24].…”

“…Three-dimensional convolution is mainly used in the segmentation of hyper-spectral data [26,27], and is also widely used in multi-spectral and hyper-spectral image fusion [25] and multi-spectral time series image processing [24]. These two enhancements allow E-UNet to segment objects with large scale differences from multi-spectral remote sensing imagery [17]. SegNet [18] is a CNN for semantic image segmentation.…”

“…In order to reduce tedious manual work and automate the processing of river segmentation and connectivity reconstruction from satellite remote sensing imagery, in this study, a cascaded river segmentation and connectivity reconstruction deep learning network model (Wa-terSCNet) was proposed to segment urban rivers and reconstruct their connectivity from Sentinel-2 multi-spectral imagery [5]. The WaterSCNet model was improved from the E-UNet model [17] by adding a soft attention gate mechanism [45] to the encoder-decoder structure. The soft attention gate mechanism, which includes channel attention [46], spatial attention [47], or a combination of both [48], can be trained by gradient backpropagation [49] and has been extensively studied and implemented in intelligent image recognition tasks [50,51].…”