Papers Aerial Scene Classification
“Aerial Scene Classification” 태그가 달린 논문 22편 · 필터 해제
TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios
Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses. In this…
Aerial Scene ClassificationCPUGPUImage Classification+4Gradient-Guided Multiscale Focal Attention Network for Remote Sensing Scene Classification
Remote sensing scene classification (RSSC) aims to understand and analyze the semantic information at the scene level with complex geographical properties. Despite the profound success of advanced deep models in automati…
Aerial Scene ClassificationRemote Sensing Image ClassificationScene ClassificationScaling Efficient Masked Image Modeling on Large Remote Sensing Dataset
Masked Image Modeling (MIM) has become an essential method for building foundational visual models in remote sensing (RS). However, the limitations in size and diversity of existing RS datasets restrict the ability of MI…
Aerial Scene ClassificationDiversityobject-detectionObject Detection+3MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods …
Aerial Scene ClassificationBuilding change detection for remote sensing imagesChange DetectionChange detection for remote sensing images+13Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification
Aerial scene classification, which aims to semantically label remote sensing images in a set of predefined classes (e.g., agricultural, beach, and harbor), is a very challenging task in remote sensing due to high intra-c…
Aerial Scene ClassificationClassificationimage-classificationImage Classification+2All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification
Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly and 2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how …
Aerial Scene ClassificationAllMultiple Instance LearningScene ClassificationAdvancing Plain Vision Transformer Towards Remote Sensing Foundation Model
Large-scale vision foundation models have made significant progress in visual tasks on natural images, with vision transformers being the primary choice due to their good scalability and representation ability. However, …
Aerial Scene ClassificationFew-Shot LearningObject Detection In Aerial ImagesSemantic SegmentationLearning Instance Representation Banks for Aerial Scene Classification
Aerial scenes are more complicated in terms of object distribution and spatial arrangement than natural scenes due to the bird view, and thus remain challenging to learn discriminative scene representation. Recent soluti…
Aerial Scene ClassificationClassificationMultiple Instance LearningScene ClassificationAll Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly; 2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how to …
Aerial Scene ClassificationAllImage ClassificationMultiple Instance Learning+2An Empirical Study of Remote Sensing Pretraining
Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with the ImageNet pretrained weights…
Aerial Scene ClassificationBuilding change detection for remote sensing imagesChange DetectionChange detection for remote sensing images+4A Multi-Stage Duplex Fusion ConvNet for Aerial Scene Classification
Existing deep learning based methods effectively prompt the performance of aerial scene classification. However, due to the large amount of parameters and computational cost, it is rather difficult to apply these methods…
Aerial Scene ClassificationScene ClassificationAerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling
Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure of the image content, e.g., by assigning a semantic label to every pixel of the image. With the popularization of data-driven …
Aerial Scene ClassificationBenchmarkingClassificationMulti-Task Learning+2Local semantic enhanced convnet for aerial scene recognition
Aerial scene recognition is challenging due to the complicated object distribution and spatial arrangement in a large-scale aerial image. Recent studies attempt to explore the local semantic representation capability of …
Aerial Scene ClassificationImage ClassificationScene ClassificationScene RecognitionA Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification
In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strategy, named ReLU-Based Feature Fusion (RBFF…
Aerial Scene ClassificationClassificationDimensionality Reductionimage-classification+3HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks
Hexagonal CNN models have shown superior performance in applications such as IACT data analysis and aerial scene classification due to their better rotation symmetry and reduced anisotropy. In order to realize hexagonal …
Aerial Scene ClassificationScene ClassificationA multiple-instance densely-connected ConvNet for aerial scene classification
In contrast with nature scenes, aerial scenes are often composed of many objects crowdedly distributed on the surface in bird’s view, the description of which usually demands more discriminative features as well as local…
Aerial Scene ClassificationClassificationImage ClassificationMultiple Instance Learning+2Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote a…
Aerial Scene ClassificationGeneral Classificationimage-classificationImage Classification+2AID++: An Updated Version of AID on Scene Classification
Aerial image scene classification is a fundamental problem for understanding high-resolution remote sensing images and has become an active research task in the field of remote sensing due to its important role in a wide…
Aerial Scene ClassificationClassificationDiversityGeneral Classification+1HexaConv
The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems. Recently, it was shown that CNNs can exploit oth…
Aerial Scene ClassificationScene ClassificationAttention-based Deep Multiple Instance Learning
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the b…
Aerial Scene ClassificationMultiple Instance Learning