Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%

Hand-drawn sketch and vector map matching based on topological features

In the process of addressing, when people use words to express indistinctly, they often draw simple sketches to assist expression, which helps people to form a simple spatial scene in the brain and correspond to the actual scene one by one, and finally locate and find the target address. How to establish an one-to-one mapping relationship between the spatial objects in the hand-drawn sketch and in the vector map is the key to the realization of map addressing and location, and this process is also the process of map matching. This paper aims to address difficult problems associated with the features of hand-drawn sketches and vector map matching in order to improve the use of all potential matching points designed for application in hand-drawn sketches and spatial relation matrix structures of vector maps. To accomplish this, we use the N-queen problem solving process and improve the tabu search algorithm. In the matching process under the constraint of a single spatial relationship, and the hierarchical matching process under the constraint of multiple spatial relations, this study verifies the quality of the spatial relationship and the feasibility and effectiveness of the matching method of hand-drawn sketches and vector maps using the improved tabu search algorithm

Prominent Size Effects without a Depolarization Field Observed in Ultrathin Ferroelectric Oxide Membranes

The increasing miniaturization of electronics requires a better understanding of material properties at the nanoscale. Many studies have shown that there is a ferroelectric size limit in oxides, below which the ferroelectricity will be strongly suppressed due to the depolarization field, and whether such a limit still exists in the absence of the depolarization field remains unclear. Here, by applying uniaxial strain, we obtain pure in-plane polarized ferroelectricity in ultrathin SrTiO3 membranes, providing a clean system with high tunability to explore ferroelectric size effects especially the thickness-dependent ferroelectric instability with no depolarization field. Surprisingly, the domain size, ferroelectric transition temperature, and critical strain for room-temperature ferroelectricity all exhibit significant thickness dependence. These results indicate that the stability of ferroelectricity is suppressed (enhanced) by increasing the surface or bulk ratio (strain), which can be explained by considering the thickness-dependent dipole-dipole interactions within the transverse Ising model. Our study provides new insights into ferroelectric size effects and sheds light on the applications of ferroelectric thin films in nanoelectronics