Digital farming approach merges new technologies and sensor data to optimize the quality of crop monitoring in agriculture. The successful fusion of technology and data is highly dependent on the parameter collection, the modeling adoption, and the technology integration being accurately implemented according to the specified needs of the farm. This fusion technique has not yet been widely adopted due to several challenges; however, our study here reviews current methods and applications for fusing technologies and data. First, the study highlights different sensors that can be merged with other systems to develop fusion methods, such as optical, thermal infrared, multispectral, hyperspectral, light detection and ranging and radar. Second, the data fusion using the internet of things is reviewed. Third, the study shows different platforms that can be used as a source for the fusion of technologies, such as ground-based (tractors and robots), space-borne (satellites) and aerial (unmanned aerial vehicles) monitoring platforms. Finally, the study presents data fusion methods for site-specific crop parameter monitoring, such as nitrogen, chlorophyll, leaf area index, and aboveground biomass, and shows how the fusion of technologies and data can improve the monitoring of these parameters. The study further reveals limitations of the previous technologies and provides recommendations on how to improve their fusion with the best available sensors. The study reveals that among different data fusion methods, sensors and technologies, the airborne and terrestrial LiDAR fusion method for crop, canopy, and ground may be considered as a futuristic easy-to-use and low-cost solution to enhance the site-specific monitoring of crop parameters.

/pdf/technology-and-data-fusion-methods-to-enhance-site-specific-2qgmza6a.pdf

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Hyperspectral image (HSI) super-resolution generally means the fusion of low-spatial resolution hyperspectral image (LRHSI) and high-spatial resolution multi-spectral (MSI)/panchromatic (PAN) image (HRMPI) to get high-spatial resolution HSI (HRHSI). Existing fusion methods have not sufficiently considered the huge spectral and spatial resolution difference between the LRHSI and HRMPI. In addition, most DL-based methods that adopt the CNN structure are limited by its local feature learning, and it is difficult to exploit the global dependency of image features. To fully adapt to the huge modality difference between LRHSI and HRMPI and release the limitation of local feature learning, we design the cross spectral-scale and shift-window based cross spatial-scale non-local attention networks (CSSNet) to effectively fuse the LRHSI and HRMPI. These two networks could explicitly learn the spectral and spatial correlations between two input images. These correlations are then used to reconstruct the HRHSI feature, which makes the obtained HRHSI feature to maintain the spectral and spatial feature consistency with the input images. Finally, a ‘feature aggregation module’ is designed to aggregate the image features from these two networks and output the fused HRHSI. Extensive experimental results on both HM-fusion (fusion with MSI) and HP-fusion (fusion with PAN image) tasks demonstrate CSSNet’s state-of-the-art (SOTA) performance compared to other fusion methods. (The codes could be available at https://github.com/rs-lsl/CSSNet). 

Hyperspectral Image Super-Resolution Network Based on Cross-Scale Nonlocal Attention

The fusion between the low-resolution hyperspectral image (LRHSI) and the panchromatic (PAN) image could obtain the high-resolution hyperspectral image (HRHSI). Recently, deep learning (DL)-based fusion methods have been explored widely due to their powerful feature learning ability. However, most DL-based methods that use the one-step fusion manner can suffer from the blurring effect. In addition, they have not fully utilized the spatial and spectral feature information of two input images, which hinders the improvement of the resulting image quality. Therefore, to fully mitigate the blurring effect and utilize two input images, we propose a dual conditional diffusion models-based fusion network (DCDMF) to obtain the fused HRHSI. The conditional diffusion model (CDM) can generate the high-quality image with realistic details in an iterative denoising manner (in the inference sampling stage) other than the one-step fusion manner, which could mitigate the blurring effect greatly. To improve the spatial and spectral fidelity of the fused HRHSI, we propose the dual spatial and spectral CDM (DCDM) (two noise prediction networks with different conditional input) to, respectively, extract the image feature from the LRHSI and PAN images with different image characteristics and reconstruct the corresponding HRHSI feature and fuse them. In addition, considering the high-dimensional property of the hyperspectral image (HSI), we pretrain an auto-encoder to encode the HSI into the low-dimensional latent space with more discriminate features to reduce the computational cost. Based on the auto-encoder, we also perform the image generation process in the residual latent space to focus on learning the residual latent spatial details. Extensive experimental results on three datasets show the superiority of our method over several state-of-the-art (SOTA) methods. (The Ziyuan (ZY) dataset and codes could be available at https://github.com/rs-lsl/DCDMF)

Hyperspectral and Panchromatic Images Fusion Based on the Dual Conditional Diffusion Models

The hyperspectral image (HSI) has been widely used in many applications due to its fruitful spectral information. However, the limitation of imaging sensors has reduced its spatial resolution that causes detail loss. One solution is to fuse the low spatial resolution HSI (LR-HSI) and the panchromatic (PAN) image with inverse features to get the high-resolution HSI (HR-HSI). Most of the existing fusion methods just focus on small fusion ratios like 4 or 6, which might be impractical for some large ratios’ HSI and PAN image pairs. Moreover, the ill-posedness of restoring detail information in HSI with hundreds of bands from PAN image with only one band has not been solved effectively, especially under large fusion ratios. Therefore, a lightweight unmixing-based PAN-guided fusion network (Pgnet) is proposed to mitigate this ill-posedness and improve the fusion performance significantly. Note that the fusion process of the proposed network is under the projected low-dimensional abundance subspace with an extremely large fusion ratio of 16. Furthermore, based on the linear and nonlinear relationships between the PAN intensity and abundance, an interpretable PAN detail inject network (PDIN) is designed to inject the PAN details into the abundance feature efficiently. Comprehensive experiments on simulated and real datasets demonstrate the superiority and generality of our method over several state-of-the-art (SOTA) methods qualitatively and quantitatively (the codes in pytorch and paddle versions and dataset could be available at https://github.com/rs-lsl/Pgnet).

Unmixing-Based PAN-Guided Fusion Network for Hyperspectral Imagery

The hyperspectral image (HSI) has been widely used in many applications due to its fruitful spectral information. However, the limitation of imaging sensors has reduced its spatial resolution that causes detail loss. One solution is to fuse the low spatial resolution hyperspectral image (LR-HSI) and the panchromatic image (PAN) with inverse features to get the high-resolution hyperspectral image (HR-HSI). Most of the existing fusion methods just focus on small fusion ratios like 4 or 6, which might be impractical for some large ratios' HSI and PAN image pairs. Moreover, the ill-posedness of restoring detail information in HSI with hundreds of bands from PAN image with only one band has not been solved effectively, especially under large fusion ratios. Therefore, a lightweight unmixing-based pan-guided fusion network (Pgnet) is proposed to mitigate this ill-posedness and improve the fusion performance significantly. Note that the fusion process of the proposed network is under the projected low-dimensional abundance subspace with an extremely large fusion ratio of 16. Furthermore, based on the linear and nonlinear relationships between the PAN intensity and abundance, an interpretable PAN detail inject network (PDIN) is designed to inject the PAN details into the abundance feature efficiently. Comprehensive experiments on simulated and real datasets demonstrate the superiority and generality of our method over several state-of-the-art (SOTA) methods qualitatively and quantitatively (The codes in pytorch and paddle versions and dataset could be available at https://github.com/rs-lsl/Pgnet). (This is a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block.) 

/pdf/unmixing-based-pan-guided-fusion-network-for-hyperspectral-17r0wa14.pdf

Spaceborne hyperspectral images are expected to have abundant applications in various fields, particularly in the quantitative observation of the Earth. However, the problem of low spatial resolution has limited their effectiveness to some extent. There are urgent needs for high-spatial-resolution hyperspectral images. We explore fusing the hyperspectral image with panchromatic and multispectral images captured by sensors onboard ZY-1 (02D) to obtain high-spatial-resolution and high-spectral-resolution images. Four approaches to obtain the final high-spatial-resolution hyperspectral image data are proposed, and six fusion methods are used. The fusion results were evaluated using quantitative indicators and classification performance, and image quality greatly improved after fusion. The fusion approach that used multispectral image data as an intermediate layer twice in the fusion process obtained the best fusion effect, and this was verified by both quantitative and application evaluation results. This provides an effective approach to obtain a high-quality, high-spatial-resolution hyperspectral image using the combination of panchromatic, multispectral, and hyperspectral images acquired by the sensors onboard ZY-1(02D).

Study on ground object classification based on the hyperspectral fusion images of ZY-1(02D) satellite

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Study on ground object classification based on the hyperspectral fusion images of ZY-1(02D) satellite