5,861 research outputs found
Large kernel spectral and spatial attention networks for hyperspectral image classification.
Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI
Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
Deep learning-based hyperspectral image (HSI) classification and object
detection techniques have gained significant attention due to their vital role
in image content analysis, interpretation, and wider HSI applications. However,
current hyperspectral object detection approaches predominantly emphasize
either spectral or spatial information, overlooking the valuable complementary
relationship between these two aspects. In this study, we present a novel
\textbf{S}pectral-\textbf{S}patial \textbf{A}ggregation (S2ADet) object
detector that effectively harnesses the rich spectral and spatial complementary
information inherent in hyperspectral images. S2ADet comprises a hyperspectral
information decoupling (HID) module, a two-stream feature extraction network,
and a one-stage detection head. The HID module processes hyperspectral images
by aggregating spectral and spatial information via band selection and
principal components analysis, consequently reducing redundancy. Based on the
acquired spatial and spectral aggregation information, we propose a feature
aggregation two-stream network for interacting spectral-spatial features.
Furthermore, to address the limitations of existing databases, we annotate an
extensive dataset, designated as HOD3K, containing 3,242 hyperspectral images
captured across diverse real-world scenes and encompassing three object
classes. These images possess a resolution of 512x256 pixels and cover 16 bands
ranging from 470 nm to 620 nm. Comprehensive experiments on two datasets
demonstrate that S2ADet surpasses existing state-of-the-art methods, achieving
robust and reliable results. The demo code and dataset of this work are
publicly available at \url{https://github.com/hexiao-cs/S2ADet}
Spectral-Spatial Graph Reasoning Network for Hyperspectral Image Classification
In this paper, we propose a spectral-spatial graph reasoning network (SSGRN)
for hyperspectral image (HSI) classification. Concretely, this network contains
two parts that separately named spatial graph reasoning subnetwork (SAGRN) and
spectral graph reasoning subnetwork (SEGRN) to capture the spatial and spectral
graph contexts, respectively. Different from the previous approaches
implementing superpixel segmentation on the original image or attempting to
obtain the category features under the guide of label image, we perform the
superpixel segmentation on intermediate features of the network to adaptively
produce the homogeneous regions to get the effective descriptors. Then, we
adopt a similar idea in spectral part that reasonably aggregating the channels
to generate spectral descriptors for spectral graph contexts capturing. All
graph reasoning procedures in SAGRN and SEGRN are achieved through graph
convolution. To guarantee the global perception ability of the proposed
methods, all adjacent matrices in graph reasoning are obtained with the help of
non-local self-attention mechanism. At last, by combining the extracted spatial
and spectral graph contexts, we obtain the SSGRN to achieve a high accuracy
classification. Extensive quantitative and qualitative experiments on three
public HSI benchmarks demonstrate the competitiveness of the proposed methods
compared with other state-of-the-art approaches
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
This paper proposes a novel deep learning framework named
bidirectional-convolutional long short term memory (Bi-CLSTM) network to
automatically learn the spectral-spatial feature from hyperspectral images
(HSIs). In the network, the issue of spectral feature extraction is considered
as a sequence learning problem, and a recurrent connection operator across the
spectral domain is used to address it. Meanwhile, inspired from the widely used
convolutional neural network (CNN), a convolution operator across the spatial
domain is incorporated into the network to extract the spatial feature.
Besides, to sufficiently capture the spectral information, a bidirectional
recurrent connection is proposed. In the classification phase, the learned
features are concatenated into a vector and fed to a softmax classifier via a
fully-connected operator. To validate the effectiveness of the proposed
Bi-CLSTM framework, we compare it with several state-of-the-art methods,
including the CNN framework, on three widely used HSIs. The obtained results
show that Bi-CLSTM can improve the classification performance as compared to
other methods
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