Weilian Zhou 교수

Most methods based on the convolutional neural network show satisfying performance for hyperspectral image (HSI) classification. However, the spatial dependence among different pixels is not well learned by CNNs. A recurrent neural network (RNN) can effectively establish the dependence of nonadjacent pixels and ensure that each feature activation in its output is an activation at the specific location concerning the whole image, in contrast to the usual local context window in the CNNs. However,

The goal of hyperspectral image (HSI) classification is to assign land-cover labels to each HSI pixel in a patch-wise manner. Recently, sequential models, such as recurrent neural networks (RNN), have been developed as HSI classifiers which need to scan the HSI patch into a pixel-sequence with the scanning order first. However, RNNs have a biased ordering that cannot effectively allocate attention to each pixel in the sequence, and previous methods that use multiple scanning orders to average th

As the specialty of hyperspectral image (HSI), it consists of 2D spatial and 1D spectral information. In the field of deep learning, HSI classification is an appealing research topic. Many existing methods process the HSI in spatial or spectral domain separately, which cannot fully extract the representative features, and the most used 3D convolutional neural network (3D-CNN) will suffer from mixing up complex spectral information. In this paper, we propose a spatial-spectral unified method by u

This study introduces an innovative approach in hyperspectral imaging (HSI) classification by integrating convolution, recurrence, and self-attention mechanisms in a 3D configuration. We address several challenges such as the 1) disruption of spectral continuity by traditional dimensionality reduction methods like PCA, 2) the overlooking of band-to-band continuous features in existing spatial-only 2D multiscanning strategy, and 3) the limitations in model design by simply cascading recurrent neu

Vision Transformer (ViT) has been introduced into the computer vision (CV) field with its self-attention mechanism to capture global dependency. However, simply deploying ViT on a hyperspectral image (HSI) classification task can not get satisfying results because ViT is a spatial-only self-attention model, but rich spectral information exists in HSI. Moreover, most HSI classifiers integrate spectral and spatial features in a cascaded flowchart, ignoring the internal correlation between spectral

Vision Transformer (ViT) has recently been introduced into the computer vision (CV) field with its self-attention mechanism and gotten remarkable performance. However, simply applying ViT for hyperspectral image (HSI) classification is not applicable due to 1) ViT is a spatial-only self-attention model, but rich spectral information exists in HSI; 2) ViT needs sufficient training samples, but HSI suffers from limited samples; 3) ViT does not well learn local features; 4) multi-scale features for

Convex clustering is an appealing approach to many classical clustering problems. It stands out among standard methods as it enjoys the existence of a unique global optimal solution. Despite this advantage, convex clustering has not been widely adopted, due to its computationally intensive nature. To address this obstacle, especially in the “big data” setting, we introduce a Scalable cOnvex cLustering AlgoRithm via Parallel Coordinate Descent Method (SOLAR-PCDM) that improves the algorithm’s sca

Hyperspectral images (HSIs) consists of 2D spatial information and 1D spectral signature due to its specialty. Most models take the raw spectral signature as the input directly by regarding the spectral data as a sequence, which cannot fully explore the redundant and complementary information inside the spectral bands. In this paper, we proposed a novel sub-band grouping recurrent neural network (RNN) model with gated recurrent units (GRUs) to find the intrinsic feature in spectral information.

Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored to this task, offering a unique viewpoint. However, several challenges persist 1) RNNs struggle with centric feature aggregation and are sensitive to interfering pixels, 2) Transformers require sig

Hong Kong's compact urban landscape, marked by high-rise buildings and dense traffic, poses significant challenges for managing street-level air quality.Traditional stationary monitoring systems often lack the spatial resolution needed to capture the dynamic variability of air pollution in complex street canyons.To address this, this study proposes a novel framework that integrates a mobile sensing platform, computer vision, and machine learning for dynamic air quality monitoring.Using a sensor-

Variance estimation is an important aspect in statistical inference, especially in the dependent data situations. Resampling methods are ideal for solving this problem since these do not require restrictive distributional assumptions. In this paper, we develop a novel resampling method in the Jackknife family called the stationary jackknife . It can be used to estimate the variance of a statistic in the cases where observations are from a general stationary sequence. Unlike the moving block jack

Hyperspectral image (HSI) classification is a cornerstone of remote sensing, enabling precise material and land-cover identification through rich spectral information. While deep learning has driven significant progress in this task, small patch-based classifiers, which account for over 90% of the progress, face limitations: (1) the small patch (e.g., 7x7, 9x9)-based sampling approach considers a limited receptive field, resulting in insufficient spatial structural information critical for objec