Showing 1-25 of 9,643 resultsfor "Index Terms":Remote Sensing Image Classification
"Index Terms":Remote Sensing Image Classification
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Detection of Land Cover Changes using Satellite Image Classification Technique
Puligadda Pavan;B Shanmukha Sai Varma;Koyagura Asish;Manne Suneetha
2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)
Year: 2023 | Conference Paper |
The land cover of an area refers to its surface coverage, such as grasslands, housing areas, or water bodies. Changes take place in the structure of a land cover over time due to various natural environmental variables and human actions. In addition, it affects the life cycle of that region and the plans of human authorities for that piece of land. In order to detect the changes in a land cover qu...Show More
An Overview on Remote Sensing Image Classification Methods with a Focus on Support Vector Machine
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
With the growing demand for better performance of remote sensing (RS) image classification, a variety of methods have been proposed in RS image classification field in recent years. In general, there are two categories of RS image classification methods: pixel-based (PB) approach and object-based (OB) approach. In this paper, RS image classification methods are reviewed from the perspective of PB ...Show More
Application of Convolutional Neural Networks in Remote Sensing Image Classification
2019 2nd International Conference on Safety Produce Informatization (IICSPI)
Year: 2019 | Conference Paper |
Aiming at the problem of remote sensing image classification, this paper designs an improved convolutional neural network structure. Combined with the transfer learning method, the classification experiments of different remote sensing image datasets are compared, and the effectiveness and versatility of the proposed method are verified.Show More
Parameter Selection for Segmentation in Object-Oriented Classification of Remotely Sensed Imagery
2010 International Conference on Intelligent Computation Technology and Automation
Year: 2010 | Conference Paper |
In object-oriented classification of remote sensing imagery, image segmentation is the first step and its quality has significant effect on resulting classification. The quality of image segmentation is always controlled by user-supplied parameters. However, there is not a common way to guide the user selecting a suitable parameter for image segmentation. This paper focuses on the problem of param...Show More
A new kind of Combination Classifier and its application in Classification of Remote Sensing Image
2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA)
Year: 2018 | Conference Paper |
Aiming at the problem that each sub classifier has its own shortcomings in classification of remote sensing image, promote a new combination classifier based on the improved voting rules. The combination of different sub-classifiers is combined by cascading and parallel connection, and the voting rules based on prior knowledge are used to achieve the accurate classification of remote sensing image...Show More
A Multiscale Superpixel-Guided Filter Approach for VHR Remote Sensing Image Classification
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Year: 2019 | Conference Paper |
Cited by: Papers (1)
This paper presents a novel multiscale superpixel-guided filter (MSGF) approach for very high resolution (VHR) remote sensing image classification. Different from the traditional guided filter (GF) classification method, the proposed method utilizes a guidance image that constructed from the superpixel segmentation image, which is capable to provide more abundant and accurate edge information of l...Show More
A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Cited by: Papers (1)
This paper proposes a novel model for remote sensing image classification based on CBAM-CNN (Convolutional Block Attention Module-Convolutional Neural Network). CBAM-CNN is a well-known and effective model. However, it is limited when it comes to high-level features due to its shared spatial attention mechanism and narrow sampling range in squeeze-and-excitation module (SEM). We disentangle the sh...Show More
Improved Generative Adversarial Networks for VHR Remote Sensing Image Classification
With increasing spatial resolution of remote sensing images, accurate classification of land classes depends more on the number of labeled samples. However, the acquisition of labeled samples is difficult and time-consuming. Hence, generative adversarial networks (GANs) have become a new method for collecting training samples for very-high-resolution (VHR) remote sensing image classification. A tr...Show More
Multispectral Remote Sensing Land Use Classification Based on RBF Neural Network with Parameters Optimized by Genetic Algorithm
2018 International Conference on Sensor Networks and Signal Processing (SNSP)
Year: 2018 | Conference Paper |
Cited by: Papers (4)
In view of the problems existing in the application of multispectral image classification algorithms, such as low computational speed, low accuracy and difficult convergence, a multispectral remote sensing image classification method based on RBF neural network with parameters optimized by genetic algorithm (GA) is proposed in this paper. This model uses the multispectral sensing remote data and f...Show More
Multiple Embeddings Contrastive Pretraining for Remote Sensing Image Classification
This letter focuses on remote sensing image interpretation and aims to promote the use of contrastive self-supervised learning (SSL) in varied applications of remote sensing image classification. The proposed method is a contrastive self-supervised pretraining framework that encourages the network to learn image representations by comparing image embeddings extracted by different encoders and pred...Show More
Toward Dataset Construction for Remote Sensing Image Interpretation
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Year: 2021 | Conference Paper |
Cited by: Papers (2)
With the rapid advancement of remote sensing (RS) technology, RS image interpretation has made great progress and been widely used in broad applications, in which the constructed benchmark datasets for developing and testing intelligent interpretation algorithms have been playing an increasingly critical role. Motivated by the essential prerequisites of dataset in the development of RS image inter...Show More
Scene Classification of Optical Remote Sensing Images Based on CNN Automatic Transfer
2018 IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
Transferring CNN model pre-trained on a large scale visual dataset is a current hot topic research for Optical Remote Sensing Image Scene Classification (ORSISC). In the process of transferring pre-trained CNN model, it is difficult to obtain the best fine-tuning hyper-parameters by manual experiences. To solve this problem, CNN automatic fine-tuning based on reinforcement learning is put forward ...Show More
Importance of CNN in the Classification of Remote Sensing Images
2023 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)
Year: 2023 | Conference Paper |
In this paper we can observe how CNN plays an important role in the classification of remote sensing images. Here we have discussed about different methods that are available for classification of images. The commonly used remote sensing dataset are mentioned here and the structure of convolutional neural network is described. The current state of the art of classification of remote sensing images...Show More
Classification of Remote Sensing Scenes Based on Neural Architecture Search Network
2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
Remote sensing image scene classification plays an important role in remote sensing image retrieval, land-use identification and urban planning. Deep learning brings great opportunity to the research in this field, but it transfers the difficulty of traditional characteristic engineering to the design of network structure. In this paper, we focus on the automatic design of the network model and pr...Show More
Classifying High Resolution Remote Sensing Images by Fine-Tuned VGG Deep Networks
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (50)
Deep convolutional networks perform well in remote sensing (RS) image classification. Usually, it is difficult to obtain a large number of labeled samples in remote sensing classification tasks. Traditionally, the acquisition of remote sensing images is quite different from the photos provided by digital cameras. However, the imaging system for high resolution (HR) RS images (often with RGB 3 chan...Show More
The classification of remote sensing images has always been one of the important tasks in the field of remote sensing. However, with the improvement of sensors, the spatial resolution of the remote sensing images we obtained has been further improved, so that the images after the classification of high-resolution remote sensing images will appear jagged and salt-and pepper noise phenomena. In orde...Show More
Remote Sensing Scene Classification Based on Global and Local Consistent Network
Jingjing Ma;Qiushuo Ma;Xu Tang;Xiangrong Zhang;Cheng Zhu;Qunnie Peng;Licheng Jiao
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Year: 2020 | Conference Paper |
Cited by: Papers (7)
Scene classification of remote sensing (RS) images has attracted increasing attention due to its wide applications. Recently, with the advances of deep learning models, especially convolutional neural networks (CNNs), the performance of remote sensing image scene classification has been significantly improved. In this paper, based on the popular CNN, we develop a new scene classification network, ...Show More
Topic Model for Remote Sensing Data: A Comprehensive Review
Qiqi Zhu;Jiangqin Wan;Yanfei Zhong;Qingfeng Guan;Liangpei Zhang;Deren Li
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Year: 2020 | Conference Paper |
Cited by: Papers (1)
From text analysis to image interpretation, the topic model (TM) always plays an important role. With its powerful semantic mining capabilities, it is able to capture the latent spectral and spatial information from remote sensing (RS) images. Recent years have witnessed widespread use of TM to solve the problems in RS image interpretation, i.e., semantic segmentation, target detection, and scene ...Show More
A Two-Stream Unified Interpretation Network for Heterogeneous Remote Sensing Images Classification
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (1)
The conventional studies on different types of remote sensing (RS) images classifications are conducted separately. Thanks to the powerful potential of deep learning to automatically learn features from data, exploring a unified method is possible. Moreover, recent research shows that sparse and low-rank representations can convey valuable information for patterns classification. Therefore, this p...Show More
Scene Classification of Remote Sensing Images Based on Deep Network Transferability and Image Complexity
2021 International Conference on Information Technology and Biomedical Engineering (ICITBE)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
In order to solve the low efficient problem of the remote sensing image scene classification, a classification method is proposed which is based on deep network transferability (DNT) and image complexity (IC). Firstly, the pre-trained deep networks are sorted by their transferability. Then, the scenes of the image datasets are sorted by their complexity. Finally, the scenes are classified by the s...Show More
A Discriminatively Learned CNN Embedding For Remote Sensing Image Scene Classification
Wen Wang;Lijun Du;Yinxing Gao;Yanzhou Su;Feng Wang;Jian Cheng
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Year: 2019 | Conference Paper |
Cited by: Papers (1)
In this work, a discriminatively learned CNN embedding is proposed for remote sensing image scene classification. Our proposed siamese network simultaneously computes the classification loss function and the metric learning loss function of the two input images. Specifically, for the classification loss, we use the standard cross-entropy loss function to predict the classes of the images. For the ...Show More
Research on Remote Sensing Image Terrain Classification Algorithm Based on Improved KNN
2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
In recent years, with the development of machine learning technology, neural networks have gradually become a convenient method for classification of remote sensing image features. This article briefly describes the structure and principle of the process of remote sensing image feature recognition, using three remote sensing image data sets AID, NWPU-RESISC45, UC Merced Land Use dataset for algori...Show More
Improved Attention Mechanism and Residual Network for Remote Sensing Image Scene Classification
Jiayuan Kong;Yurong Gao;Yanjun Zhang;Huimin Lei;Yao Wang;Hesheng Zhang
Aiming at the problem that the accuracy of traditional remote sensing image classification model is not ideal, a classification method based on improved attention mechanism and residual network is proposed. In order to prevent overfitting, we choose the network structure of ResNet18 as the framework. Meanwhile, ResNet18 has a small number of parameters and a fast calculation speed. Then, a paralle...Show More
Semi-Supervised Scene Classification for Remote Sensing Images Based on CNN and Ensemble Learning
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (2)
The special characteristic of remote sensing (RS) images being large scale while only low number of labeled samples available in practical applications has been obstacle to the development of RS image classification. In this paper, a novel semi-supervised framework is proposed. The high-capacity convolutional neural networks (CNN) are adopted to extract preliminary image features. The strategy of ...Show More
Road Safety Evaluation Using Remote Sensing Techniques
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Year: 2019 | Conference Paper |
This study aims to utilize remote sensing techniques to evaluate the accuracy of Cloverleaf interchanges in the city of Jeddah, Saudi Arabia. The objectives of the study are: (1) to investigate the use of pixel-based and object-based image classification to extract the Cloverleaf interchanges from a high-resolution satellite image. (2) To study the relationship between the extracted Cloverleaf int...Show More
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