Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R²) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R² = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R² = 0.834, RMSE = 0.824) and actual reference LAI (R² = 0.659, RMSE = 0.697).

Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein network (ERGW-net) for small road object detection in infrared aerial images. This method aims to resolve problems of small object size, low contrast, few object features, and occlusions. The ERGW-net adopts the advantages of ResNet, Inception net, and YOLOv8 networks to improve object detection efficiency and accuracy by improving the structure of the backbone, neck, and loss function. The ERGW-net was tested on a DroneVehicle dataset with a large sample size and the HIT-UAV dataset with a relatively small sample size. The results show that the detection accuracy of different road targets (e.g., pedestrians, cars, buses, and trucks) is greater than 80%, which is higher than the existing methods.

At present, the LiDAR ground filtering technology is very mature. There are fewer applications in 3D-object detection due to the limitations of filtering accuracy and efficiency. If the ground can be removed quickly and accurately, the 3D-object detection algorithm can detect objects more accurately and quickly. In order to meet the application requirements of 3D-object detection, inspired by Universal-RANSAC, we analyze the detailed steps of RANSAC and propose a precise and efficient RANSAC-based ground filtering method. The principle of GroupSAC is analyzed, and the sampled points are grouped by attributes to make it easier to sample the correct point. Based on this principle, we devise a method for limiting sampled points that is applicable to point clouds. We describe preemptive RANSAC in detail. Its breadth-first strategy is adopted to obtain the optimal plane without complex iterations. We use the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets and the KITTI dataset for testing. Experiments show that our method has higher filtering accuracy and efficiency compared with the currently widely used methods. We explore the application of ground filtering methods in 3D-object detection, and the experimental results show that our method can improve the object detection accuracy without affecting the efficiency.

Aiming at the existing problem of unmanned aerial vehicle (UAV) aerial photography for riders’ helmet wearing detection, a novel aerial remote sensing detection paradigm is proposed by combining super-resolution reconstruction, residual transformer-spatial attention, and you only look once version 5 (YOLOv5) image classifier. Due to its small target size, significant size change, and strong motion blur in UAV aerial images, the helmet detection model for riders has weak generalization ability and low accuracy. First, a ladder-type multi-attention network (LMNet) for target detection is designed to conquer these difficulties. The LMNet enables information interaction and fusion at each stage, fully extracts image features, and minimizes information loss. Second, the Residual Transformer 3D-spatial Attention Module (RT3DsAM) is proposed in this work, which digests information from global data that is important for feature representation and final classification detection. It also builds self-attention and enhances correlation between information. Third, the rider images detected by LMNet are cropped out and reconstructed by the enhanced super-resolution generative adversarial networks (ESRGAN) to restore more realistic texture information and sharp edges. Finally, the reconstructed images of riders are classified by the YOLOv5 classifier. The results of the experiment show that, when compared with the existing methods, our method improves the detection accuracy of riders’ helmets in aerial photography scenes, with the target detection mean average precision (mAP) evaluation indicator reaching 91.67%, and the image classification top1 accuracy (TOP1 ACC) gaining 94.23%.

Limited to the low spatial resolution of the hyperspectral imaging sensor, mixed pixels are inevitable in hyperspectral images. Therefore, to obtain the endmembers and corresponding fractions in mixed pixels, hyperspectral unmixing becomes a hot spot in the field of remote sensing. Endmember spectral variability (ESV), which is common in hyperspectral images, affects spectral unmixing accuracy. This paper proposes a spectral unmixing method based on maximum margin criterion and derivative weights (MDWSU) to reduce the effect of ESV on spectral unmixing. Firstly, in the MDWSU model, an effective and fast algorithm is employed for establishing the endmember spectral library. Then a spectral weighting matrix based on the maximum margin criterion is constructed based on the endmember spectral library. Besides, derivative analysis and local neighborhood weights are merged into local neighborhood derivative weights, which act as a regularization term to penalize different abundance vectors. Local neighborhood derivative weights and spectral weighting matrix are proved to reduce the effect of ESV. Real hyperspectral data experiments show that the MDWSU model can obtain more accurate endmembers and abundance estimation. In addition, the experimental results, including the spectral angle distance and the root mean square error, prove the superiority of the MDWSU model over the previous methods.

This paper proposes a framework for unmixing of hyperspectral data that is based on utilizing the scattering transform to extract deep features that are then used within a neural network. Previous research has shown that using the scattering transform combined with a traditional K-nearest neighbors classifier (STFHU) is able to achieve more accurate unmixing results compared to a convolutional neural network (CNN) applied directly to the hyperspectral images. This paper further explores hyperspectral unmixing in limited training data scenarios, which are likely to occur in practical applications where the access to large amounts of labeled training data is not possible. Here, it is proposed to combine the scattering transform with the attention-based residual neural network (ResNet). Experimental results on three HSI datasets demonstrate that this approach provides at least 40% higher unmixing accuracy compared to the previous STFHU and CNN algorithms when using limited training data, ranging from 5% to 30%, are available. The use of the scattering transform for deriving features within the ResNet unmixing system also leads more than 25% improvement when unmixing hyperspectral data contaminated by additive noise.