Recently, team member -Shaocheng’s paper titled “Joint Learning of Frequency and Spatial Domains for Dense Image Prediction” has been published in the top-tier journal in the field of Geomatics - ISPRS Journal of Photogrammetry and Remote Sensing. In this work, we fully explore frequency domain learning and propose a joint learning paradigm of frequency and spatial domains. This paradigm can take full advantage of the combined preponderances of frequency learning and spatial learning; specifically, frequency and spatial domain learning can effectively capture intrinsic global and local information, respectively. To achieve this, an innovative but effective linear learning block is proposed to conduct the learning process directly in the frequency domain. Together with the prevailing spatial learning operation, i.e., convolution, a powerful and scalable joint learning framework is established.
Here’s a Share Link – a personalized URL providing free access to the article Share Link.
team member -Shaocheng’s paper titled “Self-Supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness” has been published in the top-tier journal in the field of Transportation - IEEE Transactions on Intelligent Transportation Systems. In this paper, we present DLNet for pixel-wise depth estimation, which simultaneously extracts global and local features with the aid of our depth Linformer block. This block consists of the Linformer and innovative soft split multi-layer perceptron blocks. Moreover, a three-dimensional geometry smoothness loss is proposed to predict a geometrically natural depth map by imposing the second-order smoothness constraint on the predicted three-dimensional point clouds, thereby realizing improved performance as a byproduct. Finally, we explore the multi-scale prediction strategy and propose the maximum margin dual-scale prediction strategy for further performance improvement.
Here’s a Link – a URL providing access to the article Share Link.
team member -Sani’s paper titled “Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning” has been published in the top-tier journal in the field of Geomatics - ISPRS Open Journal of Photogrammetry and Remote Sensing. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset.
Here’s a Share Link – a URL providing free access to the article Share Link.
Congratulations to Shaocheng and Sani!