EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS

Deep learning models achieve excellent semantic segmentation results for airborne laser scanning (ALS) point clouds, if sufficient training data are provided. Increasing amounts of annotated data are becoming publicly available thanks to contributors from all over the world. However, models trained on a specific dataset typically exhibit poor performance on other datasets. I.e., there are significant domain shifts, as data captured in different environments or by distinct sensors have different distributions. In this work, we study this domain shift and potential strategies to mitigate it, using two popular ALS datasets: the ISPRS Vaihingen benchmark from Germany and the LASDU benchmark from China. We compare different training strategies for cross-city ALS point cloud semantic segmentation. In our experiments, we analyse three factors that may lead to domain shift and affect the learning: point cloud density, LiDAR intensity, and the role of data augmentation. Moreover, we evaluate a well-known standard method of domain adaptation, deep CORAL (Sun and Saenko, 2016). In our experiments, adapting the point cloud density and appropriate data augmentation both help to reduce the domain gap and improve segmentation accuracy. On the contrary, intensity features can bring an improvement within a dataset, but deteriorate the generalisation across datasets. Deep CORAL does not further improve the accuracy over the simple adaptation of density and data augmentation, although it can mitigate the impact of improperly chosen point density, intensity features, and further dataset biases like lack of diversity.

P-Norm Attention Deep CORAL: Extending Correlation Alignment Using Attention and the P-Norm Loss Function

CORrelation ALignment (CORAL) is an unsupervised domain adaptation method that uses a linear transformation to align the covariances of source and target domains. Deep CORAL extends CORAL with a nonlinear transformation using a deep neural network and adds CORAL loss as a part of the total loss to align the covariances of source and target domains. However, there are still two problems to be solved in Deep CORAL: features extracted from AlexNet are not always a good representation of the original data, as well as joint training combined with both the classification and CORAL loss may not be efficient enough to align the distribution of the source and target domain. In this paper, we proposed two strategies: attention to improve the quality of feature maps and the p-norm loss function to align the distribution of the source and target features, further reducing the offset caused by the classification loss function. Experiments on the Office-31 dataset indicate that our proposed methodologies improved Deep CORAL in terms of performance.

Tosanoides annepatrice, a new basslet from deep coral reefs in Micronesia (Perciformes, Percoidei, Serranidae)

The new species Tosanoidesannepatricesp. n. is described from four specimens collected at depths of 115–148 m near Palau and Pohnpei in Micronesia. It differs from the other three species of this genus in life color and in certain morphological characters, such as body depth, snout length, anterior three dorsal-fin spine lengths, caudal-fin length, and other characters. There are also genetic differences from the other four species of Tosanoides (d ≈ 0.04–0.12 in mtDNA cytochrome oxidase I). This species is presently known only from Palau and Pohnpei within Micronesia, but it likely occurs elsewhere throughout the tropical western Pacific.