Learning hyperspectral images from RGB images via a coarse-to-fine CNN

Mei, Shaohui; Geng, Yunhao; Hou, Junhui; Du, Qian

doi:10.1007/s11432-020-3102-9

Cited by 62 publications

(12 citation statements)

References 31 publications

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“…There are two types of quality assessments used to evaluate pansharpening, namely, reduced resolution and full resolution. Please note that the pansharpening results should satisfy two metrics, including consistency and synthesis [2]. The consistency shows that the pansharpening result once degraded at the original MS resolution, should be spectrally similar to the original MS image as much as possible.…”

Section: A Experimental Designmentioning

confidence: 99%

“…The MS images have rich spectral information, but a continuous improvement in the spectral resolution of MS images affects their spatial resolution. Remote-sensing image processing has developed many topics, such as super-resolution [2]- [4], feature extraction [5], cloud removal [6], and classification [7]. In addition, the pansharpening technique [8]- [10] has been proposed to improve the spatial resolution of an MS image using a high spatial resolution PAN image.…”

Section: Introductionmentioning

confidence: 99%

“…The MRA-based methods inject spatial information in the MS images. Since the MRA methods do not require the training data and are more effective in retaining the spectral characteristics of the original MS image than the CS, they have been relatively successful and popular for implementing pansharpening techniques [2]. The MRA-based methods include the additive wavelet luminance proportional (AWLP) [30], smoothing filter based on intensity modulation (SFIM) [31], and morphological filters (MF) [32].…”

Section: Introductionmentioning

confidence: 99%

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Multiresolution Analysis Based on Dual-Scale Regression for Pansharpening

Wang

Yao

Cai

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Pansharpening technique is used to merge the original multispectral image (MS) with a high spatial resolution panchromatic image (PAN). Due to its robustness, the multiresolution analysis (MRA) is an important part of pansharpening. The scale regression model is effective for improving MRA. However, the existing MRA based on scale regression results into single-scale regression information, thus affecting the final pansharpening result. To address this problem, in this work, we propose a dual-scale regression-based MRA for pansharpening. First, we establish a scale regression-based model. Then, this model is improved using a high-pass modulation (HPM) injection scheme. Finally, the dual-scale information is added to the scale regression to construct the dual-scale regression for obtaining the final pansharpening result. We perform experiments using five datasets. The results show that the proposed method obtains a better pansharpening result as compared to various state-of-theart MRA methods. In addition, the quantitative and qualitative analysis of the results shows that the proposed method achieves appropriate spatial and spectral resolution fusion. Therefore, it has a great potential in pansharpening technique.

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Section: A Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiresolution Analysis Based on Dual-Scale Regression for Pansharpening

Wang

Yao

Cai

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Alternatively, hyperspectral data may also be utilized in this context for spotting a forest fire in a larger region [14][15][16]. However, it has a low temporal and spatial resolution, thus narrowing down the surveillance area [17,18]. The most often employed method is to acquire visible or infrared images via exploration flights with planes or low-cost drones [19,20].…”

Section: Related Workmentioning

confidence: 99%

Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Harkat

Nascimento

Bernardino³

et al. 2022

Remote Sensing

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Wildfire early detection and prevention had become a priority. Detection using Internet of Things (IoT) sensors, however, is expensive in practical situations. The majority of present wildfire detection research focuses on segmentation and detection. The developed machine learning models deploy appropriate image processing techniques to enhance the detection outputs. As a result, the time necessary for data processing is drastically reduced, as the time required rises exponentially with the size of the captured pictures. In a real-time fire emergency, it is critical to notice the fire pixels and warn the firemen as soon as possible to handle the problem more quickly. The present study addresses the challenge mentioned above by implementing an on-site detection system that detects fire pixels in real-time in the given scenario. The proposed approach is accomplished using Deeplabv3+, a deep learning architecture that is an enhanced version of an existing model. However, present work fine-tuned the Deeplabv3 model through various experimental trials that have resulted in improved performance. Two public aerial datasets, the Corsican dataset and FLAME, and one private dataset, Firefront Gestosa, were used for experimental trials in this work with different backbones. To conclude, the selected model trained with ResNet-50 and Dice loss attains a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%.

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“…Object detection based on convolutional neural network(CNN) which has make huge improvements over traditional machine learning in the field of remote sensing such as hyperspectral image classification [3]- [6], anomaly detection [7], are divided into two factions(namely, anchor-based [8]and anchor-free [9]). In this paper, we mainly focus on the mature and early-developing object detection algorithms for the anchor box mechanism.…”

mentioning

confidence: 99%

Finding Nonrigid Tiny Person With Densely Cropped and Local Attention Object Detector Networks in Low-Altitude Aerial Images

Zhang

Feng

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Finding tiny persons under the drone vision was, is and remains to be an integral and challenging task. Unmanned Aerial Vehicles (UAVs) with high-speed, low-altitude and multiperspective flight bring about violently various scales of objects, which burdens the optimization of models. Moreover, the detection performance of densely and faintly discernible person characteristics is far less than that of large objects in highresolution aerial images. In this paper, we introduce the image cropping strategy and attention mechanism based on YOLOv5 to address small person detection in the optimized VisDrone2019 dataset. Specifically, we propose a Densely Cropped and Local Attention of object detector Network (DCLANet), which is inspired by the observation that less area occupied by small objects should be fully focused and relatively magnified in the original image. DCLANet assembled Density Map Guided Object Detection (DMNet) in Aerial Images and You Only Look Twice (YOLT): Rapid Multi-Scale Object Detection In Satellite Imagery to crop images upon training and testing stage, meanwhile, added Bottleneck Attention Mechanism (BAM) to YOLOv5 baseline framework, which more focus on person objects other than irrelevant categories. To achieve further improvement of DCLANet, we also provide bags of useful strategies: data augmentation, label fusion, category filtering and hyperparameter evolution. Extensive experiments on the VisDrone2019 show that DCLANet achieves state-of-the-art performance, the detection result of person category AP val @0.5 is 50.04% with test-dev subset, which is substantially better than the previous SOTA method(DPNetV3) by 12.01%. In addition, on our optimized VisDrone2019 dataset, AP

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Learning hyperspectral images from RGB images via a coarse-to-fine CNN

Cited by 62 publications

References 31 publications

Multiresolution Analysis Based on Dual-Scale Regression for Pansharpening

Multiresolution Analysis Based on Dual-Scale Regression for Pansharpening

Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Finding Nonrigid Tiny Person With Densely Cropped and Local Attention Object Detector Networks in Low-Altitude Aerial Images

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