Object detection in remote sensing images (RSIs) often suffers from several increasing challenges, including the large variation in object scales and the diverse-ranging context. Prior methods tried to address these challenges by expanding the spatial receptive field of the backbone, either through large-kernel convolution or dilated convolution. However, the former typically introduces considerable background noise, while the latter risks generating overly sparse feature representations. In this paper, we introduce the Poly Kernel Inception Network (PKINet) to handle the above challenges. PKINet employs multi-scale convolution kernels without dilation to extract object features of varying scales and capture local context. In addition, a Context Anchor Attention (CAA) module is introduced in parallel to capture long-range contextual information. These two components work jointly to advance the performance of PKINet on four challenging remote sensing detection benchmarks, namely DOTA-v1.0, DOTA-v1.5, HRSC2016, and DIOR-R.
Majority models of remote sensing image changing detection can only get great effect in a specific resolution data set. With the purpose of improving change detection effectiveness of the model in the multi-resolution data set, a weighted rich-scale inception coder network (WRICNet) is proposed in this article, which can make a great fusion of shallow multi-scale features, and deep multi-scale features. The weighted rich-scale inception module of the proposed can obtain shallow multi-scale features, the weighted rich-scale coder module can obtain deep multi-scale features. The weighted scale b…
PKINet-v2: Towards Powerful and Efficient Poly-Kernel Remote Sensing Object Detection
Nanjing University of Science and Technology（南京理工大学）；Zhejiang University（浙江大学）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电子与计算机工程系）；State Key Lab. of Intelligent Manufacturing of Advanced Construction Machinery（先进施工机械智能制造国家重点实验室）
PolyFootNet: Extracting Polygonal Building Footprints in Off-Nadir Remote Sensing Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Applied Machine Learning Lab, School of Data Science, City University of Hong Kong（香港城市大学数据科学学院应用机器学习实验室）
Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing
Chair of Data Science in Earth Observation, Technical University of Munich（地球观测数据科学教授职位，慕尼黑技术大学）；Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；School of Engineering and Design, Technical University of Munich (TUM)（工程与设计学院，慕尼黑技术大学（TUM））
PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction from High-Resolution Remote Sensing Images
China University of Petroleum (East China)（中国石油大学（华东））；South Surveying&Mapping Instrument Co.,Ltd.（南方测绘仪器有限公司）；China Railway Design Corporation（中国铁路设计集团有限公司）
HiT: Building Mapping with Hierarchical Transformers
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）
Northwestern Polytechnical University（西北工业大学）；The University of Adelaide（阿德莱德大学）
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets are generally captured by various remote sensors and have different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and thus it is prone to overfitting when using deep CNNs. In this paper, we first deliver a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, AINet can …
University of Science, VNU-HCM（胡志明市国家大学科学大学）；John von Neumann Institute, VNU-HCM（胡志明市国家大学约翰·冯·诺依曼研究所）；Vietnam National University, Ho Chi Minh city（越南国家大学胡志明市分校）
The Medico: Multimedia Task 2020 focuses on developing an efficient and accurate computer-aided diagnosis system for automatic segmentation [3]. We participate in task 1, Polyps segmentation task, which is to develop algorithms for segmenting polyps on a comprehensive dataset. In this task, we propose methods combining Residual module, Inception module, Adaptive Convolutional neural network with U-Net model, and PraNet for semantic segmentation of various types of polyps in endoscopic images. We select 5 runs with different architecture and parameters in our methods. Our methods show potential…
Austrian Institute of Technology（奥地利技术研究所）；HCM University of Technology（胡志明市理工大学）；University of Essex（埃塞克斯大学）；FPT Soft Company（FPT软件公司）；Singapore Institute of Technology（新加坡理工大学）
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first valuate various benchmark convolutional neural network (CNN) architectures: MobileNet V1/V2, ResNet 50/151V2, InceptionV3/InceptionResNetV2, EfficientNet B0/B7, DenseNet 121/201, ConNeXt Tiny/Large. Then, the best performing models are selected to train a compact model in a teacher-student arrangement. The knowledge distillation from the teacher aims to achieve high performance…
German Aerospace Center (DLR)（德国航空航天中心）；Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）；Technical University of Munich (TUM)（慕尼黑工业大学）
Building segmentation is of great importance in the task of remote sensing imagery interpretation. However, the existing semantic segmentation and instance segmentation methods often lead to segmentation masks with blurred boundaries. In this paper, we propose a novel instance segmentation network for building segmentation in high-resolution remote sensing images. More specifically, we consider segmenting an individual building as detecting several keypoints. The detected keypoints are subsequently reformulated as a closed polygon, which is the semantic boundary of the building. By doing so, t…
Carnegie Mellon University（卡内基梅隆大学）；Google Research（谷歌研究院）；University of California, Berkeley（加州大学伯克利分校）
In recent years, we have seen tremendous progress in the field of object detection. Most of the recent improvements have been achieved by targeting deeper feedforward networks. However, many hard object categories such as bottle, remote, etc. require representation of fine details and not just coarse, semantic representations. But most of these fine details are lost in the early convolutional layers. What we need is a way to incorporate finer details from lower layers into the detection architecture. Skip connections have been proposed to combine high-level and low-level features, but we argue…
Manifold Constraint Regularization for Remote Sensing Image Generation
Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences（中国科学院大学中国科学院软件研究所）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology（香港科技大学）
本文首次提出流形约束正则化方法，通过新度量评估数据流形结构并约束 GAN 训练，缓解遥感图像生成中的过拟合问题，同时提升生成质量与下游任务性能。
Satellite images are often contaminated by clouds. Cloud removal has received much attention due to the wide range of satellite image applications. As the clouds thicken, the process of removing the clouds becomes more challenging. In such cases, using auxiliary images such as near-infrared or synthetic aperture radar (SAR) for reconstructing is common. In this study, we attempt to solve the problem using two generative adversarial networks (GANs). The first translates SAR images into optical images, and the second removes clouds using the translated images of prior GAN. Also, we propose dilat…
P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（中国石油大学（华东）海洋与空间信息学院）；School of Surveying and Geoinformation Engineering, East China University of Technology（东华理工大学测绘与地理信息工程学院）
Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models
Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗莱贝格资源技术研究所）；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心）；Linköping University（林雪平大学）
University of Manouba（马努巴大学）；Taibah University（塔伊巴大学）；Prince Sultan University（苏丹王子大学）；Arab Open University（阿拉伯开放大学）；University of Sfax（斯法克斯大学）
Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar…
Convolutional neural networks (CNNs) have shown great promise in improving computer aided detection (CADe). From classifying tumors found via mammography as benign or malignant to automated detection of colorectal polyps in CT colonography, these advances have helped reduce the need for further evaluation with invasive testing and prevent errors from missed diagnoses by acting as a second observer in today's fast paced and high volume clinical environment. CADe methods have become faster and more precise thanks to innovations in deep learning over the past several years. With advancements such…
Remote sensing data imputation using deep learning for multispectral imagery
Water Research Centre, University of New South Wales（新南威尔士大学水研究中心）；ARC ITTC Data Analytics for Resources and Environments, University of New South Wales（新南威尔士大学资源与环境数据分析师联盟）；Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, University of New South Wales（新南威尔士大学数学与统计学过渡人工智能研究组）
Vector Map as Language: Toward Unified Remote Sensing Vector Mapping
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Geomatics Engineering, Changsha University of Science and Technology（长沙理工大学测绘工程系）；State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）
Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image…
Norwegian University of Science and Technology (NTNU)（挪威科技大学（NTNU））；Oslo University Hospital（奥斯陆大学医院）；University of Oslo (UiO)（奥斯陆大学（UiO））
Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image …
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformat…
VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs
School of Remote Sensing and Information Engineering（遥感与信息工程学院）；College of Oceanography and Space Informatics（海洋学与空间信息学院）；School of Surveying and Geoinformation Engineering（测绘与地理信息工程学院）
University of Florida（佛罗里达大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when targets are of irregular shape or difficult to distinguish from background or neighboring objects. Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management. However, tree crowns in remote sensing imagery are often difficult to label and annotate due to irregular shape, overlapping canopies, shadowing, and indistinct edges. There are also multiple approaches to anno…
