PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery

Satellite imagery analysis plays a vital role in remote sensing, but the information loss caused by cloud cover seriously hinders its application. This study presents a high-performance cloud removal architecture called Progressive Multi-scale Attention Autoencoder (PMAA), which simultaneously leverages global and local information. It mainly consists of a cloud detection backbone and a cloud removal module. The cloud detection backbone uses cloud masks to reinforce cloudy areas to prompt the cloud removal module. The cloud removal module mainly comprises a novel Multi-scale Attention Module (MAM) and a Local Interaction Module (LIM). PMAA establishes the long-range dependency of multi-scale features using MAM and modulates the reconstruction of the fine-grained details using LIM, allowing for the simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale feature representation, PMAA outperforms the previous state-of-the-art model CTGAN consistently on the Sen2_MTC_Old and Sen2_MTC_New datasets. Furthermore, PMAA has a considerable efficiency advantage, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These extensive results highlight the potential of PMAA as a lightweight cloud removal network suitable for deployment on edge devices. We will release the code and trained models to facilitate the study in this direction.Comment: 8 pages, 5 figure

ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction

Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It holds significant importance in numerous domains, including traffic flow prediction and weather forecasting. However, existing methods face challenges in handling spatiotemporal correlations, as they commonly adopt encoder and decoder architectures with identical receptive fields, which adversely affects prediction accuracy. This paper proposes an Asymmetric Receptive Field Autoencoder (ARFA) model to address this issue. Specifically, we design corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we introduce a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. To address the scarcity of meteorological prediction data, we constructed the RainBench, a large-scale radar echo dataset specific to the unique precipitation characteristics of inland regions in China for precipitation prediction. Experimental results demonstrate that ARFA achieves consistent state-of-the-art performance on two mainstream spatiotemporal prediction datasets and our RainBench dataset, affirming the effectiveness of our approach. This work not only explores a novel method from the perspective of receptive fields but also provides data support for precipitation prediction, thereby advancing future research in spatiotemporal prediction.Comment: 0 pages, 5 figure

High-Fidelity Lake Extraction via Two-Stage Prompt Enhancement: Establishing a Novel Baseline and Benchmark

The extraction of lakes from remote sensing images is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a unified prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, which involves prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embeddings through self- and cross-attention in the prompt decoder. Prompts are deactivated once the model is trained to ensure independence during inference, enabling automated lake extraction. Evaluations on Surface Water and Qinghai-Tibet Plateau Lake datasets show consistent performance improvements compared to the previous state-of-the-art method. LEPrompter achieves mIoU scores of 91.48% and 97.43% on the respective datasets without introducing additional parameters or GFLOPs. Supplementary materials provide the source code, pre-trained models, and detailed user studies.Comment: 8 pages, 7 figure

PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region

The Three-River-Source region is a highly significant natural reserve in China that harbors a plethora of untamed botanical resources. To meet the practical requirements of botanical research and intelligent plant management, we construct a large-scale dataset for Plant detection in the Three-River-Source region (PTRS). This dataset comprises 6965 high-resolution images of 2160*3840 pixels, captured by diverse sensors and platforms, and featuring objects of varying shapes and sizes. Subsequently, a team of botanical image interpretation experts annotated these images with 21 commonly occurring object categories. The fully annotated PTRS images contain 122, 300 instances of plant leaves, each labeled by a horizontal rectangle. The PTRS presents us with challenges such as dense occlusion, varying leaf resolutions, and high feature similarity among plants, prompting us to develop a novel object detection network named PlantDet. This network employs a window-based efficient self-attention module (ST block) to generate robust feature representation at multiple scales, improving the detection efficiency for small and densely-occluded objects. Our experimental results validate the efficacy of our proposed plant detection benchmark, with a precision of 88.1%, a mean average precision (mAP) of 77.6%, and a higher recall compared to the baseline. Additionally, our method effectively overcomes the issue of missing small objects. We intend to share our data and code with interested parties to advance further research in this field.Comment: 10 pages, 5 figure

In situ atomic scale mechanisms of strain-induced twin boundary shear to high angle grain boundary in nanocrystalline Pt

Twin boundary can both strengthen and soften nanocrystalline metals and has been an important path for improving the strength and ductility of nano materials. Here, using in-lab developed double-tilt tensile stage in the transmission electron microscope, the atomic scale twin boundary shearing process was in situ observed in a twin-structured nanocrystalline Pt. It was revealed that the twin boundary shear was resulted from partial dislocation emissions on the intersected {111} planes, which accommodate as large as 47% shear strain. It is uncovered that the partial dislocations nucleated and glided on the two intersecting {111} slip planes lead to a transition of the original symmetric tilt ∑3/(111) coherent twin boundary into a symmetric tilt ∑9/(114) high angle grain boundary. These results provide insight of twin boundary strengthening mechanisms for accommodating plasticity strains in nanocrystalline metals

Plastic deformation through dislocation saturation in ultrasmall Pt nanocrystals and its in situ atomistic mechanisms

The atomic-scale deformation dynamic behaviors of Pt nanocrystals with size of similar to 18 nm were in situ investigated using our homemade device in a high-resolution transmission electron microscope. It was discovered that the plastic deformation of the nanosized single crystalline Pt commenced with dislocation "appreciation" first, then followed by a dislocation "saturation" phenomenon. The magnitude of strain plays a key role on dislocation behaviors. At the early to medium stage of deformation, the plastic deformation was controlled by the full dislocation activities accompanied by the formation of Lomer dislocation locks from reaction of full dislocations. When the strain increased to a significant level, stacking faults and extended dislocations as well as Lomer-Cottrell locks appeared. The Lomer-Cottrell locks can unlock through transferring into Lomer dislocation locks first, and then Lomer dislocation locks were destructed under high stresses. The very high density dislocations and the frequent dislocation reactions through Lomer dislocations and Lomer-Cottrell locks may lead to work hardening in nanosized Pt

Accurate breast cancer diagnosis using a stable feature ranking algorithm

Abstract Background Breast cancer (BC) is one of the most common cancers among women. Since diverse features can be collected, how to stably select the powerful ones for accurate BC diagnosis remains challenging. Methods A hybrid framework is designed for successively investigating both feature ranking (FR) stability and cancer diagnosis effectiveness. Specifically, on 4 BC datasets (BCDR-F03, WDBC, GSE10810 and GSE15852), the stability of 23 FR algorithms is evaluated via an advanced estimator (S), and the predictive power of the stable feature ranks is further tested by using different machine learning classifiers. Results Experimental results identify 3 algorithms achieving good stability ( $$S \ge 0.55$$ S ≥ 0.55 ) on the four datasets and generalized Fisher score (GFS) leading to state-of-the-art performance. Moreover, GFS ranks suggest that shape features are crucial in BC image analysis (BCDR-F03 and WDBC) and that using a few genes can well differentiate benign and malignant tumor cases (GSE10810 and GSE15852). Conclusions The proposed framework recognizes a stable FR algorithm for accurate BC diagnosis. Stable and effective features could deepen the understanding of BC diagnosis and related decision-making applications

Reveal the size effect on the plasticity of ultra-small sized Ag nanowires with in situ atomic-scale microscopy

Revealing the atomic-scale deformation mechanisms of metallic nanowires (NWs) is important for their practical application. However, there are few reports providing direct atomic-scale experimental elucidation on those metallic NWs. Here, we conduct serial in situ deformation tests on silver (Ag) nanowires with diameters of 3-11 nm. The in situ atomic-scale observations reveal a transition in the deformation mechanism with a decrease in the diameter of Ag NWs. For the [5 (5) over bar4] and [001] oriented NWs with diameters of similar to 11 nm, the plastic deformation is dominated by full dislocation that involves leading and trailing partial dislocations, whereas the full or extended dislocations are rarely observed in the NWs with diameters in the range of similar to 5-8 nm, and their plastic deformation is governed by SF generation and annihilation. Moreover, for the [(1) over bar 11] oriented NWs, 60 degrees mixed and pure edge dislocations are frequently observed when the diameter is approximately 5 nm and the plastic deformation is accommodated by relative slip between two adjacent {111} planes for NWs with diameters below similar to 3 nm. These results indicate that the plastic deformation not only depends on the size of NWs but also can be significantly impacted by the loading orientation. (C) 2016 Elsevier B.V. All rights reserved