144 research outputs found
Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification
Unsupervised learning methods for feature extraction are becoming more and
more popular. We combine the popular contrastive learning method (prototypical
contrastive learning) and the classic representation learning method
(autoencoder) to design an unsupervised feature learning network for
hyperspectral classification. Experiments have proved that our two proposed
autoencoder networks have good feature learning capabilities by themselves, and
the contrastive learning network we designed can better combine the features of
the two to learn more representative features. As a result, our method
surpasses other comparison methods in the hyperspectral classification
experiments, including some supervised methods. Moreover, our method maintains
a fast feature extraction speed than baseline methods. In addition, our method
reduces the requirements for huge computing resources, separates feature
extraction and contrastive learning, and allows more researchers to conduct
research and experiments on unsupervised contrastive learning
Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression
In the U.S., corn is the most produced crop and has been an essential part of
the American diet. To meet the demand for supply chain management and regional
food security, accurate and timely large-scale corn yield prediction is
attracting more attention in precision agriculture. Recently, remote sensing
technology and machine learning methods have been widely explored for crop
yield prediction. Currently, most county-level yield prediction models use
county-level mean variables for prediction, ignoring much detailed information.
Moreover, inconsistent spatial resolution between crop area and satellite
sensors results in mixed pixels, which may decrease the prediction accuracy.
Only a few works have addressed the mixed pixels problem in large-scale crop
yield prediction. To address the information loss and mixed pixels problem, we
developed a variational autoencoder (VAE) based multiple instance regression
(MIR) model for large-scaled corn yield prediction. We use all unlabeled data
to train a VAE and the well-trained VAE for anomaly detection. As a preprocess
method, anomaly detection can help MIR find a better representation of every
bag than traditional MIR methods, thus better performing in large-scale corn
yield prediction. Our experiments showed that variational autoencoder based
multiple instance regression (VAEMIR) outperformed all baseline methods in
large-scale corn yield prediction. Though a suitable meta parameter is
required, VAEMIR shows excellent potential in feature learning and extraction
for large-scale corn yield prediction
Probability-based Global Cross-modal Upsampling for Pansharpening
Pansharpening is an essential preprocessing step for remote sensing image
processing. Although deep learning (DL) approaches performed well on this task,
current upsampling methods used in these approaches only utilize the local
information of each pixel in the low-resolution multispectral (LRMS) image
while neglecting to exploit its global information as well as the cross-modal
information of the guiding panchromatic (PAN) image, which limits their
performance improvement. To address this issue, this paper develops a novel
probability-based global cross-modal upsampling (PGCU) method for
pan-sharpening. Precisely, we first formulate the PGCU method from a
probabilistic perspective and then design an efficient network module to
implement it by fully utilizing the information mentioned above while
simultaneously considering the channel specificity. The PGCU module consists of
three blocks, i.e., information extraction (IE), distribution and expectation
estimation (DEE), and fine adjustment (FA). Extensive experiments verify the
superiority of the PGCU method compared with other popular upsampling methods.
Additionally, experiments also show that the PGCU module can help improve the
performance of existing SOTA deep learning pansharpening methods. The codes are
available at https://github.com/Zeyu-Zhu/PGCU.Comment: 10 pages, 5 figure
Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat
Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1 ) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops
AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention
Multi-modal medical image fusion is essential for the precise clinical
diagnosis and surgical navigation since it can merge the complementary
information in multi-modalities into a single image. The quality of the fused
image depends on the extracted single modality features as well as the fusion
rules for multi-modal information. Existing deep learning-based fusion methods
can fully exploit the semantic features of each modality, they cannot
distinguish the effective low and high frequency information of each modality
and fuse them adaptively. To address this issue, we propose AdaFuse, in which
multimodal image information is fused adaptively through frequency-guided
attention mechanism based on Fourier transform. Specifically, we propose the
cross-attention fusion (CAF) block, which adaptively fuses features of two
modalities in the spatial and frequency domains by exchanging key and query
values, and then calculates the cross-attention scores between the spatial and
frequency features to further guide the spatial-frequential information fusion.
The CAF block enhances the high-frequency features of the different modalities
so that the details in the fused images can be retained. Moreover, we design a
novel loss function composed of structure loss and content loss to preserve
both low and high frequency information. Extensive comparison experiments on
several datasets demonstrate that the proposed method outperforms
state-of-the-art methods in terms of both visual quality and quantitative
metrics. The ablation experiments also validate the effectiveness of the
proposed loss and fusion strategy
TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer
In this paper, we introduce a set of effective TOken REduction (TORE)
strategies for Transformer-based Human Mesh Recovery from monocular images.
Current SOTA performance is achieved by Transformer-based structures. However,
they suffer from high model complexity and computation cost caused by redundant
tokens. We propose token reduction strategies based on two important aspects,
i.e., the 3D geometry structure and 2D image feature, where we hierarchically
recover the mesh geometry with priors from body structure and conduct token
clustering to pass fewer but more discriminative image feature tokens to the
Transformer. As a result, our method vastly reduces the number of tokens
involved in high-complexity interactions in the Transformer, achieving
competitive accuracy of shape recovery at a significantly reduced computational
cost. We conduct extensive experiments across a wide range of benchmarks to
validate the proposed method and further demonstrate the generalizability of
our method on hand mesh recovery. Our code will be publicly available once the
paper is published
Analysis of Temporal and Spatial Evolution Characteristics of Land Subsidence in Western Songnen Plain Using Multisource Remote Sensing
AbstractThe exploitation of underground fluid is an important factor leading to land subsidence. The effects of mining depth, frequency, and mode on land subsidence are also different. The objective of this study was to develop a multisource method—including optical remote sensing interpretation, Interferometric Synthetic Aperture Radar (InSAR) technology, and unmanned aerial vehicle (UAV)—to reveal the long-term temporal and spatial evolution law of subsidence characteristics driven by groundwater and oil extraction, as well as to reveal the formation mechanism and seasonal response law of land subsidence under the action of different driving factors. In this paper, we select the western region of Jilin Province located in Songnen Plain as the study area. The subsidence funnels in the study area are distributed in a porphyritic manner, and the distribution of the subsidence funnels has a certain correlation with the distribution of the pumping wells. In farmland areas, the subsidence is mainly caused by pumping groundwater. The annual land subsidence rate in the study area is -3.14 mm/a, and the maximum deformation rate in the study area is -22.05 mm/a. The subsidence is affected by the season, shown by the fact that it rises in the dry season and decreases in the rainy season. The subsidence in the west of Songnen Plain is caused by oil pumping and groundwater pumping, and groundwater pumping is dominant. The exploitation of underground fluid transfers the pressure borne by water or oil to the soil skeleton so as to increase and consolidate the effective stress of the soil layer and lead to land subsidence. The continuous observation of the surface in the western area of Songnen Plain is helpful to guide the safe production of agriculture and industry and ensure the smooth development of local industry and agriculture
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