Jie Mei

Nankai University | NKU · College of Computer Science

Extracting roads from satellite imagery is a promising approach to update the dynamic changes of road networks efficiently and timely. However, it is challenging due to the occlusions caused by other objects and the complex traffic environment, the pixel-based methods often generate fragmented roads and fail to predict topological correctness. In t...

Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic computed tomography (CT) provides a convenient way to diagnose nodules. However, it is hard even for experienced doctors to distinguish them from the massive CT slices....

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivit...

Subspace learning (SL) plays an important role in hyperspectral image (HSI) classification, since it can provide an effective solution to reduce the redundant information in the image pixels of HSIs. Previous works about SL aim to improve the accuracy of HSI recognition. Using a large number of labeled samples, related methods can train the paramet...

Subspace learning (SL) plays an essential role in hyperspectral image (HSI) classification since it can provide an effective solution to reduce the redundant information in the image pixels of HSIs. Previous works about SL aim to improve the accuracy of HSI recognition. Using a large number of labeled samples, related methods can train the paramete...

Urban road extraction has wide applications in public transportation systems and unmanned vehicle navigation. The high-resolution remote sensing images contain background clutter and the roads have large appearance differences and complex connectivities, which makes it a very challenging task for road extraction. In this article, we propose a novel...

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivit...

Multilabel remote sensing (RS) image annotation is a challenging and time-consuming task that requires a considerable amount of expert knowledge. Most existing RS image annotation methods are based on handcrafted features and require multistage processes that are not sufficiently efficient and effective. An RS image can be assigned with a single la...

The performance of hyperspectral image (HSI) classification relies on the pixel information obtained from hundreds of contiguous and narrow spectral bands. Existing approaches, however, are limited to exploit an appropriate latent subspace for data representation within the pixel-level or superpixel-level. To utilize spectral information and spatia...

The challenges in hyperspectral image (HSI) classification lie in the existence of noisy spectral information and lack of contextual information among pixels. Considering the three different levels in HSIs, i.e., subpixel, pixel, and superpixel, offer complementary information, we develop a novel HSI feature learning network (HSINet) to learn consi...

Besides the problem of high within-class variation and between-class ambiguity in high spatial resolution (HSR) remote sensing images, the dimension of data representation is very high, which poses a challenge for scene classification. To achieve high scene classification performance, it is important to uncover a discriminative subspace for data re...

Low-rank representation (LRR) can construct the relationships among pixels for hyperspectral image (HSI) classification with a given dictionary and a noise term. However, the accuracy of HSI classification based on LRR methods is degraded with the redundant and noise information existed in pixels. The neglect of semantic information around pixels i...

To parse large-scale urban scenes using the supervised methods, a large amount of training data that can account for the vast visual and structural variance of urban environment is necessary. Unfortunately, such training data are mostly obtained by tedious and time-consuming manual work. To overcome the drawback, we propose a semisupervised learnin...

The performance of scene classification relies heavily on the spatial and structural features that are extracted from high spatial resolution remote-sensing images. Existing approaches, however, are limited in adequately exploiting latent relationships between scene images. Aiming to decrease the distances between intraclass images and increase the...

Reconstruction of 3D trees from incomplete point clouds is a challenging issue due to their large variety and natural geometric complexity. In this paper, we develop a novel method to effectively model trees from a single laser scan. First, coarse tree skeletons are extracted by utilizing the L1-median skeleton to compute the dominant direction of...

The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characte...

Modeling 3-D trees from terrestrial laser scanning (TLS) point clouds remains a challenging task for several well-known reasons, including their complex structure and severe occlusions. In order to accurately reconstruct 3-D tree models from TLS point clouds that typically suffer from significant occlusions, in this paper, a novel local structure a...