Pansharpening aims to fuse a multispectral (MS) image with low spatial resolution and a panchromatic (PAN) image with a high-spatial resolution to produce an image with both high spectral and high spatial resolution. In this study, we propose a variational pansharpening method by exploiting cartoon-texture similarities. After decomposition of the PAN image, the cartoon component always contains the global structure information, while the texture component includes the locally patterned information. This enables that the fused high-spatial resolution MS image can preserve the global and local spatial details (e.g., high-order information) well after leveraging the similarities of cartoon and texture components from PAN and MS images. To explore such cartoon-texture similarities, we describe cartoon similarity as gradient sparsity, formulated as a reweighted total variation term. Meanwhile, we use group low-rank constraint for texture similarity that is presented as repetitive texture patterns. By incorporating a data fidelity term for preserving the spectral information on the basis that the down-sampled fused MS image is consistent with the MS image, we further formulate pansharpening as an optimization problem and solve it efficiently using the alternative direction multiplier method. Extensive experiments have been conducted on a series of satellite data sets, and we also carry out a simulated vegetation coverage change experiment to verify the efficiency of the proposed method in remote sensing. The qualitative and quantitative results demonstrate that our method outperforms the state-of-the-art pansharpening methods in terms of both visual effect and objective metrics.

Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties’ yield performance, key yield-related traits such as panicle number per unit area (PNpM²) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM² trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM² trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM² trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM² trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.

Non-rigid point set registration is a fundamental problem in many fields related to computer vision, medical image processing, and pattern recognition. In this paper, we develop a new point set registration method by using an adaptive weighted objective function, which formulates the alignment of two point sets as a mixture model estimation problem. The correspondences and the transformation are jointly recovered by using the expectation-maximization (EM) algorithm to obtain promising results. Firstly, the correspondences are established using local feature descriptors, and the adaptation parameters for the mixture model are computed from these correspondences. Then, the underlying transformation is recovered by minimizing the adaptive weighted objective function deduced from the mixture model. We demonstrate the advantages of the proposed method on various types of synthetic and real data and compare the results against those obtained using the state-of-the-art methods. The experimental results show that the proposed method is robust and outperforms the other registration approaches.

Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model’s target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.

Object detection in scenes captured by unmanned aerial vehicles (UAV) is an active research area. However, the performance and efficiency of current small object detection models for UAV images are far from reaching the desired level. The inherent limitations of the features of the small objects themselves and the inconsistency of the contextual information in the feature maps lead to a degradation of the final detection performance. In this letter, to improve the performance of UAV image small object detection, we propose a multi-scale feature optimization network, named MFO-Net. We have designed three crucial modules: feature optimization fusion (FOF) module, multi-scale localized feature aggregation (MLFA) module, and feature enhancement (FE) module. FOF module enhances the fusion of features with inconsistent contexts at different levels by learning pixel-wise displacement, facilitating more effective feature fusion, which further helps focus on and capture critical information about small objects. MLFA module aggregates richer contextual information through multi-branch stripe convolution blocks, while the FE module extracts richer gradient flow information, suppresses incompatible information, and enhances feature representation capability. We conduct extensive experiments on the challenging VisDrone2019 dataset and compare the results against those obtained from the state-of-the-art methods. The experimental results show that MFO-Net performs better than other detectors. Specifically, MFO-Net achieves the best performance with 22.3% AP, 38.9% AP ₅₀ , and 22.5% AP ₇₅ on VisDrone2019. Code: https://github.com/Lanziyang121/MFO-Net.