Deep convolution neural network based approach for multispectral images

Usharani, A.; Bhavana, D.

doi:10.1007/s13198-021-01133-8

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The increasing availability of high-resolution remote sensing images in terms of both the amount of data and sensor types has focused much scholarly attention on highresolution image fusion, leading to a plethora of relevant research references in the current literature [6][7][8][9][10][11][12][13][14]. Some of the previous research has demonstrated the effectiveness of high-resolution image fusion in terms of improving classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Chen¹,

Fei²,

Wang³

et al. 2022

Sustainability

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This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms.

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Section: Introductionmentioning

confidence: 99%

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Chen¹,

Fei²,

Wang³

et al. 2022

Sustainability

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Novel fusion strategy for image fusion using rescue hunt optimization-based modified guidance model

Maddi,

Sonnad,

Hosamani

et al. 2024

International Journal of Image and Data Fusion

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Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet

Qiu

2022

Remote Sensing

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Multispectral remote sensing images have shown unique advantages in many fields, including military and civilian use. Facing the difficulty in processing cloud contaminated remote sensing images, this paper proposes a multispectral remote sensing image enhancement algorithm. A model is constructed from the aspects of cloud detection and image enhancement. In the cloud detection stage, clouds are divided into thick clouds and thin clouds according to the cloud transmitability in multi-spectral images, and a multi-layer cloud detection model is established. From the perspective of traditional image processing, a bimodal pre-detection algorithm is constructed to achieve thick cloud extraction. From the perspective of deep learning, the MobileNet algorithm structure is improved to achieve thin cloud extraction. Faced with the problem of insufficient training samples, a self-supervised network is constructed to achieve training, so as to meet the requirements of high precision and high efficiency cloud detection under the condition of small samples. In the image enhancement stage, the area where the ground objects are located is determined first. Then, from the perspective of compressed sensing, the signal is analyzed from the perspective of time and frequency domains. Specifically, the inter-frame information of hyperspectral images is analyzed to construct a sparse representation model based on the principle of compressed sensing. Finally, image enhancement is achieved. The experimental comparison between our algorithm and other algorithms shows that the average Area Overlap Measure (AOM) of the proposed algorithm reaches 0.83 and the Average Gradient (AG) of the proposed algorithm reaches 12.7, which is better than the other seven algorithms by average AG 2.

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Deep convolution neural network based approach for multispectral images

Cited by 3 publications

References 9 publications

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

Novel fusion strategy for image fusion using rescue hunt optimization-based modified guidance model

Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm Based on MobileNet

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