The timely and accurate prediction of winter wheat yields is of importance in maintaining food security. However, existing deep-learning methods used for crop yield prediction are limited. While most methods utilize recurrent neural networks (RNNs) to interpret crop time series data, they struggle to learn geographical spatial information from input data and often prove challenging to interpret with prior knowledge. In this study, hyperspectral images were used as the input to a BS-Nets network for band selection, and a graph-based RNN framework GT-long-short-term memory (LSTM) [two channels based LSTM-graph neural network (GNN)], was proposed for predicting the winter wheat yield at the county level. Using the BS-Nets for hyperspectral bands selection at the field scale, we obtained the top 4 bands in selection results for all six stages, the results were band 46 (791 nm), 50 (825 nm), 66 (954 nm), and 161 (2484 nm). Based on the results of hyperspectral bands selection, at the county scale, the similar wavelength bands of Sentinel-2 red-edge 3 (783 nm), NIR (834 nm), red-edge 4 (865 nm), SWIR2 (2190 nm) were chosen as inputs for the GT-LSTM county-level estimates of winter wheat yield. When only remote sensing data were used, the highest prediction accuracy ($R^{2}= 0.688$, RMSE = 0.54 t/ha) was obtained for DOY135 (30 days before harvest). The incorporation of the meteorological data improved the accuracy by 7% ($R^{2}= 0.714$, RMSE = 0.50 t/ha), and the optimal time for predicting wheat yield was at DOY115 (50 days before harvest). Further addition of GNN layers to the model improved the accuracy of the results by an additional 14% ($R^{2}= 0.757$, RMSE = 0.43 t/ha), and the best prediction results were then obtained at DOY105 (60 days before harvest).

Cloud contamination greatly limits the potential utilization of optical images for geoscience applications. An effective alternative is to extract data from synthetic aperture radar (SAR) images to remove clouds due to the strong penetration ability of microwaves. In this article, we propose a novel unified spatial–spectral residual network that utilizes SAR images as auxiliary data to remove clouds from optical images. The method can better establish the relationship between SAR and optical images and be divided into two modules: feature extraction and fusion module and reconstruction module. In the feature extraction and fusion module, a gated convolutional layer is introduced to discriminate cloud pixels from clean pixels, which makes up for the lack of distinguishing ability of vanilla convolutional layers and avoids the error of cloud areas in feature extraction. In the reconstruction module, spatial and channel attention mechanisms are introduced to obtain global spatial and spectral information. The network is tested on three datasets with different spatial resolutions and compositions of land covers to verify the effectiveness and applicability of the method. The results show that the method outperforms other mainstream algorithms that simultaneously use SAR images as auxiliary data with a gain of about 2.3 dB in terms of peak signal-to-noise ratio PSNR on the SEN12MS-CR dataset.

Single-image super-resolution (SISR) for high-resolution (HR) remote sensing image (RSI) acquisition is becoming increasingly valuable and important, and convolutional neural networks (CNNs) have produced considerable progress in this field. In RSIs, many similar geo-objects recur within the same scene, maintaining the same positions in both low resolution (LR) and HR. Based on this observation, we found that these similar geo-objects could be utilized to reconstruct texture details in LR by exploiting the consistent non-local relationships between these geo-objects in LR and HR, thereby improving the quality of SISR. Therefore, we propose a novel graph convolutional network (GCN) for SISR including a dynamic graph attention mechanism to learn the in-scale and cross-scale non-local features of RSIs. In scale, we propose a dynamic graph attention block (DGAB) that adaptively determines non-local patches upon the scene correlation derived from RSIs and further fuses patch-wise non-local information weighed by the attention scores of topological relationships and radiation characteristics in RSIs. Across different scales, we also introduce a dynamic graph attention mixing block (DGAMB) to upsample LR non-local information to HR non-local information. Most SISR methods have the upsampling blocks at the end of the network, ignoring feature extraction in high-dimensional space. To address this problem, DGAMB was designed as an upsampler in the middle of the model, enhancing the level of high-dimensional information extraction from the model. The experiments based on the WHU Building and UC Merced datasets show that our proposed method outperforms state-of-the-art methods. Our code is available athttps://github.com/WenjuanZhang-aircas/NSGCN.

Hyperspectral image (HSI) denoising methods that are implemented using deep learning frameworks rarely consider the intrinsic characteristics of HSIs, and often lack both physical interpretability, and generalization. In this paper, a low-rank and sparse model-based unrolled network for HSI de-noising, termed LRS-Net, is proposed. The method unrolls a model-based denoising method into a deep-unrolled network. The network is much faster than the previous method and is also able to automatically select the tuning parameters. The method inherits the advantages of model-based methods, i.e., physical interpretability and generalization, and also advantages from deep learning based methods, i.e., computational efficiency and data-based learning capabilities. Using both simulated and real HSIs it is shown the proposed method can outperform other comparative methods, both in quantitative and visual assessments.

This paper proposes a new spectral-spatial kernel minimum noise fraction transformation (SS-KMNF) as a dimensionality reduction method for hyperspectral image (HSI) classification. The kernel minimum noise fraction (KMNF) method generates new components ordered by image quality and the key index of image quality is noise fraction. In SS-KMNF, the high correlation between bands in homogeneous regions obtained using a superpixel technique is applied to improve the precision of noise fraction. Compared with the original KMNF, SS-KMNF can fully use spectral and spatial information contained in a HSI and is more effective in enhancing the performance of dimensionality reduction for HSI classification. Moreover, a new classification strategy is proposed based on superpixels which are considered as the basic unit instead of pixels for classifying features extracted by SS-KMNF. Experimental results show that SS-KMNF can get better dimensionality reduction performance than KMNF, and the superpixel is not only beneficial to precisely estimating noise fraction but can also increase the classification accuracy of features extracted by SS-KMNF. In addition, SS-KMNF can also be applied to classify transmission lines in electrical power systems.

This letter proposes a semi-supervised mixtures of factor analyzers (S2MFA) feature extraction (FE) method for hyperspectral image (HSI). S2MFA uses a Gaussian mixture model to segment the image to different regions, each region follows a Gaussian distribution and contains labeled and unlabeled samples. The method uses a factor analyzer to get a factor-loading matrix to preserve the local spatial information using the labeled and unlabeled samples. It simultaneously improves the class discrimination of the data using the labeled samples and also transforms the original image to an optimal low-dimensional subspace to achieve dimensionality reduction. The performance of the S2MFA FE method is evaluated by classification of two real HSIs and compared to different kinds of statistic unsupervised, supervised, and semi-supervised FE methods.

This article proposes a denoising method based on sparse spectral–spatial and low-rank representations (SSSLRR) using the 3-D orthogonal transform (3-DOT). SSSLRR can be effectively used to remove the Gaussian and mixed noise. SSSLRR uses 3-DOT to decompose noisy HSI to sparse transform coefficients. The 3-D discrete orthogonal wavelet transform (3-D DWT) is a representative 3-DOT suitable for denoising since it concentrates on the signal in few transform coefficients, and the 3-D discrete orthogonal cosine transform (3-D DCT) is another example. An SSSLRR using 3-D DWT will be called SSSLRR-DWT. SSSLRR-DWT is an iterative algorithm based on the alternating direction method of multipliers (ADMM) that uses sparse and nuclear norm penalties. We use an ablation study to show the effectiveness of the penalties we employ in the method. Both simulated and real hyperspectral datasets demonstrate that SSSLRR outperforms other comparative methods in quantitative and visual assessments to remove the Gaussian and mixed noise.

This paper presents a wavelet-based block low-rank representations (WBBLRR) denoising method for hyperspectral images (HSIs). WBBLRR uses 3-D wavelet transformation to decompose HSI into different blocks, where each block utilizes a low-rank representations model to obtain the denoised block, and then uses inverse 3-D wavelet transformation for all the denoised blocks to obtain the denoised HSI. The proposed method is evaluated by using both simulated and real hyperspectral datasets.

This paper presents a non-local means low-rank approximation (NLMLRA) denoising method for hyperspectral images (HSIs). NLMLRA uses a Slanted Butterworth function to construct a low-rank approximation for non-local means (NLM) operator and is efficiently implemented based on Chebyshev polynomials. The proposed method is evaluated by using both simulated and real hyperspectral datasets.

This paper presents a local spatial-spectral correlation based mixtures of factor analyzers (LSSC-MFA) denoising method for hyperspectral image (HSI). HSIs are usually degraded by different noise types such as missing lines (ML), missing pixels (MP), salt and pepper noise (SP), and Gaussian noise. The proposed method, hierarchically, removes the mixed noise. Firstly, we develop a novel local spatial-spectral correlation (LSSC) method to remove the ML noise. Then LSSC-MFA uses the mixtures of factor analyzers (MFA) method to remove the MP, SP, and Gaussian noises. The performance of the proposed method has been validated using both real and simulated HSI datasets. Results on the simulated datasets confirm considerable improvements in terms of peak signal-to-noise ratio (PSNR) compared to the state-of-the-art denoising methods used in experiments. In addition, visual improvements can be observed in the case of real dataset experiments.

This paper presents two hyperspectral image (HSI) denoising methods, mixtures of factor analyzers (MFA) and wavelet-based MFA (WMFA). MFA uses a Gaussian mixture model to segment the original HSI into different parts, where each part follows Gaussian distribution and then utilizes a factor analyzer to get a low-rank factor loading matrix, and finally uses the inverse transformation of the matrix to get the denoised hyperspectral dataset. WMFA uses the MFA in the wavelet domain to remove the noise in HSI. The proposed methods are evaluated by using both simulated and real hyperspectral datasets.

This paper presents four dimensionality reduction methods, supervised mixtures of factor analyzers (SMFA), semi-supervised mixtures of factor analyzers (S2MFA), supervised deep mixtures of factor analyzers (SDMFA) and semi-supervised deep mixtures of factor analyzers (S2DMFA), for hyperspectral image (HSI) classification. The performance of SMFA, S2MFA, SDMFA, and S2DMFA dimensionality reduction methods for classification using real HSI is evaluated in this paper. Experimental results are compared to more conventional methods like probabilistic principal component analysis, factor analysis, mixtures of factor analyzers and deep mixtures of factor analyzers and it is shown that the proposed methods give better results.

This paper presents two dimensionality reduction methods, mixtures of factor analyzers (MFA) and deep mixtures of factor analyzers (DMFA), for classification of hyperspectral image (HSI). DMFA consists of two layers of MFA and can extract more information from HSI than MFA can. The performance of MFA and DMFA dimensionality reduction methods for classification using real HSI is evaluated in this paper. Experimental results are compared to conventional methods like probabilistic principal component analysis and factor analysis and it is shown that MFA and DMFA give better results.

The added value of multiple data sources on tree species mapping has been widely analyzed. In particular, fusion of hyperspectral (HS) and LiDAR sensors for forest applications is a very hot topic. In this paper, we exploit the use of multi-scale features to fuse HS and LiDAR data for tree species mapping. Hyperspectral data is obtained from the APEX sensor with 286 spectral bands. LiDAR data has been acquired with a TopoSys sensor Harrier 56 at full waveform. We generate multi-scale features on both HS and LiDAR data, by considering the diameter and the height layer of different tree species. Experimental results on a forested area in Belgium demonstrate the effectiveness of using multi-scale features for fusion of HS image and LiDAR data both visually and quantitatively.

The kernel minimum noise fraction (KMNF) method is a nonlinear dimensionality reduction method for hyperspectral images. KMNF can transform the original data into higher dimensional feature space by using nonlinear transformation project. The key issue of KMNF is the noise estimation. The original KMNF performs noises estimation based on spatial neighborhood information. However, the spatial resolution of hyperspectral images always is not very high, and the images usually have seriously mixed pixels. Therefore, the spatial information is not enough to precisely estimate noise for KMNF. Differently, we adopt spectral correlation information which is more stable to estimate noise for KMNF. The proposed method is named the optimized KMNF method (OKMNF). Experimental results using real hyperspectral dataset demonstrate that OKMNF has much better performance than KMNF.