Papers Remote Sensing Image Classification
“Remote Sensing Image Classification” 태그가 달린 논문 109편 · 필터 해제
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. To address MLC problems, the use of deep neural networks that req…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive co…
ClassificationEarth Observationimage-classificationImage Classification+5Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network
The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has taken the important role in a wide range of a…
Deep Learningimage-classificationImage Classificationobject-detection+4Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification
Remote sensing scene classification aims to assign automatically each aerial image a specific sematic label. In this letter, we propose a new method, called self-attention-based deep feature fusion (SAFF), to aggregate d…
ClassificationRemote Sensing Image ClassificationScene ClassificationBayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification
Employing deep neural networks for Hyperspectral remote sensing (HSRS) image classification is a challenging task. HSRS images have high dimensionality and a large number of channels with substantial redundancy between c…
Classificationimage-classificationImage ClassificationRemote Sensing Image ClassificationOptical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives
In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise segmentation, change detection, and obje…
Change Detectionimage-classificationImage Classificationobject-detection+3Multimodal Fusion Transformer for Remote Sensing Image Classification
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs). As a result, many researchers have tried to incorporat…
Classificationimage-classificationImage ClassificationLand Cover Classification+1DKDFN: Domain Knowledge-Guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification
Land use and land cover maps provide fundamental information that has been used in different types of studies, ranging from public health to carbon cycling. However, the existing remote sensing image classification meth…
Decoderimage-classificationImage ClassificationLand Cover Classification+1Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide a significant value in Land Use and Land Cover (LULC) classification. The new advances in remote sensing and d…
ClassificationComputational EfficiencyData Augmentationimage-classification+4Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution
The mining and utilization of features directly affect the classification performance of models used in the classification and recognition of hyperspectral remote sensing images. Traditional models usually conduct featur…
ClassificationDimensionality Reductionimage-classificationImage Classification+1BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval
This paper presents the multi-modal BigEarthNet (BigEarthNet-MM) benchmark archive made up of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches to support the deep learning (DL) studies in multi-modal multi-label …
image-classificationImage ClassificationImage RetrievalMulti-Label Image Retrieval+3A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this prob…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle Imagery
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…
image-classificationImage ClassificationRemote Sensing Image ClassificationA Multi-Task Deep Learning Framework for Building Footprint Segmentation
The task of building footprint segmentation has been well-studied in the context of remote sensing (RS) as it provides valuable information in many aspects, however, difficulties brought by the nature of RS images such a…
Boundary DetectionDeep LearningImage ReconstructionMulti-Task Learning+3A survey of active learning algorithms for supervised remote sensing image classification
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well …
Active LearningGeneral ClassificationHyperspectral Image Classificationimage-classification+2Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding
In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes.…
image-classificationImage ClassificationRemote Sensing Image ClassificationRepresentation Learning+1Remote Sensing Image Classification with the SEN12MS Dataset
Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. Howev…
BenchmarkingClassificationGeneral Classificationimage-classification+3Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification
Deep learning (DL) has exhibited huge potentials for hyperspectral image (HSI) classification due to its powerful nonlinear modeling and end-to-end optimization characteristics. Although the superior performance of DL-ba…
ClassificationHyperspectral Image Classificationimage-classificationImage Classification+1Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification
Feature representation is an important aspect of remote-sensing based image classification. While deep convolutional neural networks are able to effectively amalgamate information, large numbers of parameters often make …
ClassificationDimensionality ReductionGeneral Classificationimage-classification+2Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) hav…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2