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Hong Huang - IEEE Xplore Author Profile

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The accurate and stable detection of infrared (IR) small targets is essential for long-range monitoring. However, current approaches struggle to effectively bridge the inherent semantic gaps between hierarchical features and emphasize their critical characteristics during multilevel feature fusion. To address this challenge, a new asymmetric intensive interactive fusion network (A \text {I}^{2} ...Show More
Deep learning (DL) has emerged as a competitive method in single-modality-dominated remote sensing (RS) data classification tasks, but its classification performance inevitably encounters a bottleneck due to the lack of representation diversity in complicated spatial structures with various land cover types. Therefore, the RS community has been actively researching multimodal feature learning tech...Show More
Hyperspectral imaging has become a popular imaging technique in the medical field, and the development of algorithms for computer-aided diagnosis (CAD) is urgently required. Traditional deep learning techniques require a lot of annotated data, which is a burden on doctors. Self-supervised learning (SSL) is a solution for extracting feature representations from unlabeled data. However, traditional ...Show More
Scene classification of very high resolution (VHR) images is an active research subject in remote sensing community, and it has provided data or decision supports for many practical applications. Although existing CNN-based methods have achieved good classification results, they have not fully exploited rich potential information contained in pre-trained models. In this paper, a novel framework te...Show More
The accurate differential diagnosis of lung nodules is critical in the early screening of lung cancer. Although deep learning-based methods have obtained good results, the large variations in sizes and shapes of nodules restrict further performance improvement in automated diagnosis. In this paper, a multi-scale multi-view model based on ensemble attention (MSMV-EA) is proposed to discriminate the...Show More
Deep color trackers mainly use pretrained convolutional neural networks (CNNs) for classification and regression, but it is difficult to discriminate targets in complex backgrounds for its limited spectral information. Compared with color video, hyperspectral videos provide better discriminative ability due to the abundant material-based information. However, it is hard to train a robust deep mode...Show More
Building extraction is a critical part of remote-sensing (RS) image interpretation, and it is a popular research topic in the RS community. However, building extraction from RS images is a difficult task due to its various shape, size, and complex scene. The extracted feature of existing deep learning methods is a lack of discrimination, resulting in incomplete buildings and irregular boundaries. ...Show More
Deep learning-based methods have demonstrated their competitive classification performance with sufficient labeled training samples. However, in practical hyperspectral image (HSI) classification applications, the labeled samples available for training are extremely limited compared with a large amount of unlabeled data, because the expert annotation of HSI is labor-intensive and time-consuming. T...Show More
Convolutional neural networks (CNNs) have achieved great success in hyperspectral image (HSI) classification. However, most CNNs require a substantial amount of manually labeled training data to yield good performances, and the expert annotation of HSI data is labor-intensive, but existing CNNs fail to effectively extract the joint spatial-spectral features from a small training set. To tackle thi...Show More
The spatial heterogeneity is an important indicator of the malignancy of lung nodules in lung cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule objects hold richer discriminative information. However, for deep learning methods driven by massive data, effectively capturing the 3D discriminative features of nodules in limited labeled samples is a challenging task...Show More
Scene classification is an active research topic in the remote sensing community, and complex spatial layouts with various types of objects bring huge challenges to classification. Convolutional neural network (CNN)-based methods attempt to explore the global features by gradually expanding the receptive field, while long-range contextual information is ignored. Vision transformer (ViT) can extrac...Show More
Hyperspectral image (HSI) classification is a hot topic in the field of remote sensing, and convolutional neural networks (CNNs) have shown good classification performance because of their capabilities of feature extraction. However, traditional CNN-based methods require a lot of labeled data during their training process, although the acquisition of labeled samples is complicated and time-consumi...Show More
Hyperspectral images (HSIs) not only possess abundant spectral features but also present a detailed spatial distribution of land cover, and they have significant advantages in the fine classification of ground materials. Recently, using convolutional neural networks (CNNs) to extract spectral–spatial features has become an effective way for HSI classification. However, conventional convolution ker...Show More
Recently, the sparse representation (SR)-based graph embedding method has been extensively used in feature extraction (FE) tasks, but it is hard to reveal the complex manifold structure and multivariate relationship of samples in the hyperspectral image (HSI). Meanwhile, the small size sample problem in HSI data also limits the performance of the traditional SR approach. To tackle this problem, th...Show More
Scene classification is an indispensable part of remote sensing image interpretation, and various convolutional neural network (CNN)-based methods have been explored to improve classification accuracy. Although they have shown good classification performance on high-resolution remote sensing (HRRS) images, discriminative ability of extracted features is still limited. In this letter, a high-perfor...Show More
Feature extraction (FE), an important preprocessing step in hyperspectral image (HSI) classification, has received growing attention in the remote sensing community. In recent years, the FE ability of deep learning (DL) methods has been widely recognized. However, most DL models focus on training networks with strong nonlinear mapping ability. They fail to explore the intrinsic manifold structure ...Show More
Scene classification of high-resolution images is an active research topic in the remote sensing community. Although convolutional neural network (CNN)-based methods have obtained good performance, large-scale changes of ground objects in complex scenes restrict the further improvement of classification accuracy. In this letter, a global–local dual-branch structure (GLDBS) is designed to explore d...Show More
Scene classification of high spatial resolution (HSR) images can provide data support for many practical applications, such as land planning and utilization, and it has been a crucial research topic in the remote sensing (RS) community. Recently, deep learning methods driven by massive data show the impressive ability of feature learning in the field of HSR scene classification, especially convolu...Show More
Recently, the convolutional neural network (CNN) has made great progress in hyperspectral image (HSI) classification because of its powerful feature extraction capability. However, the standard CNN based on grid sampling neglects the inherent relation between HSI data, which leads to poor regional edge delineation and generalization ability. Graph convolutional network (GCN) has been successfully ...Show More
Deep learning (DL) has received extensive attention from the remote sensing community in recent years due to its ability to learn deep abstract information through a hierarchical network. However, most DL methods fail to explore the local geometric structure relationship between samples within hyperspectral imagery (HSI) to improve feature extraction performance. To address this issue, a novel DL ...Show More
Scene classification is an important research topic in the field of remote sensing (RS), and deep features from convolutional neural networks (CNNs) have shown good classification performance. However, a key issue is how to effectively combine context features for further improving classification accuracy. In this letter, an end-to-end framework termed deep neural network combined with context fea...Show More
Existing scene classification methods are mainly based on high spatial resolution (HSR) images, which contain limited spectral information, and some complex scenes with similar visual perception (e.g., texture, shape and color), are prone to be misclassified. Hyperspectral images contain detailed spectral information that can realize fine classification of ground objects by mining subtle spectral ...Show More
In this paper, we develop a CNN-GCN joint network (CGJNet) to learn global scene features and context information of high resolution remote sensing (HRRS) images. The proposed CGJNet method is composed of two streams, including CNN-stream (C-stream) and GCN-stream (G-stream). In the C-stream, a variation of the DenseNet-121 is developed to describe global visual information of HRRS images. In the ...Show More
In this letter, a new semisupervised dimensionality reduction (DR) method, termed geodesic-based manifold joint hypergraphs (GMJHs), is proposed for hyperspectral image (HSI). This method first builds a geodesic-based reconstruction model to discover the nonlinear similarity between two manifold reconstruction neighborhoods. Then, it implies the probabilistic relationship between unlabeled samples...Show More
Sparse representation-based graph embedding methods have been successfully applied to dimensionality reduction (DR) in recent years. However, these approaches usually become problematic in the presence of the hyperspectral image (HSI) that contains complex nonlinear manifold structure. Inspired by recent progress in manifold learning and hypergraph framework, a novel DR method named local constrai...Show More