scholarly journals Novel Multi-Scale Filter Profile-Based Framework for VHR Remote Sensing Image Classification

2019 ◽  
Vol 11 (18) ◽  
pp. 2153
Author(s):  
Zhiyong Lv ◽  
Guangfei Li ◽  
Yixiang Chen ◽  
Jón Atli Benediktsson
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Remote Sensing Image Classification ◽  
Very High Spatial Resolution ◽  
Layer Stacking ◽  
Initial Classification

Filter is a well-known tool for noise reduction of very high spatial resolution (VHR) remote sensing images. However, a single-scale filter usually demonstrates limitations in covering various targets with different sizes and shapes in a given image scene. A novel method called multi-scale filter profile (MFP)-based framework (MFPF) is introduced in this study to improve the classification performance of a remote sensing image of VHR and address the aforementioned problem. First, an adaptive filter is extended with a series of parameters for MFP construction. Then, a layer-stacking technique is used to concatenate the MPFs and all the features into a stacked vector. Afterward, principal component analysis, a classical descending dimension algorithm, is performed on the fused profiles to reduce the redundancy of the stacked vector. Finally, the spatial adaptive region of each filter in the MFPs is used for post-processing of the obtained initial classification map through a supervised classifier. This process aims to revise the initial classification map and generate a final classification map. Experimental results performed on the three real VHR remote sensing images demonstrate the effectiveness of the proposed MFPF in comparison with the state-of-the-art methods. Hard-tuning parameters are unnecessary in the application of the proposed approach. Thus, such a method can be conveniently applied in real applications.

scholarly journals Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

2020 ◽  
Vol 12 (6) ◽  
pp. 1012 ◽  
Author(s):  
Cheng Shi ◽  
Zhiyong Lv ◽  
Xiuhong Yang ◽  
Pengfei Xu ◽  
Irfana Bibi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Supervised Learning ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification ◽  
Very High

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 174 ◽  
Author(s):  
Han Liu ◽  
Jun Li ◽  
Lin He ◽  
Yu Wang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Data ◽  
Sampling Strategy ◽  
Remote Sensing Image ◽  
Fine Tuning ◽  
Spatial Dependency ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification

Irregular spatial dependency is one of the major characteristics of remote sensing images, which brings about challenges for classification tasks. Deep supervised models such as convolutional neural networks (CNNs) have shown great capacity for remote sensing image classification. However, they generally require a huge labeled training set for the fine tuning of a deep neural network. To handle the irregular spatial dependency of remote sensing images and mitigate the conflict between limited labeled samples and training demand, we design a superpixel-guided layer-wise embedding CNN (SLE-CNN) for remote sensing image classification, which can efficiently exploit the information from both labeled and unlabeled samples. With the superpixel-guided sampling strategy for unlabeled samples, we can achieve an automatic determination of the neighborhood covering for a spatial dependency system and thus adapting to real scenes of remote sensing images. In the designed network, two types of loss costs are combined for the training of CNN, i.e., supervised cross entropy and unsupervised reconstruction cost on both labeled and unlabeled samples, respectively. Our experimental results are conducted with three types of remote sensing data, including hyperspectral, multispectral, and synthetic aperture radar (SAR) images. The designed SLE-CNN achieves excellent classification performance in all cases with a limited labeled training set, suggesting its good potential for remote sensing image classification.

scholarly journals A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 399-403
Author(s):  
Linmei Wu ◽  
Li Shen ◽  
Zhipeng Li
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Topic Model ◽  
High Spatial Resolution ◽  
Kernel Method ◽  
Remote Sensing Image ◽  
Structure Information ◽  
Remote Sensing Image Classification ◽  
Very High Spatial Resolution ◽  
Very High

A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. The final kernel function is defined as <i>K</i>&thinsp;=&thinsp;<i>u<sub>1</sub></i><i>K</i><sup>spec</sup>&thinsp;+&thinsp;<i>u<sub>2</sub></i><i>K</i><sup>spat</sup>&thinsp;+&thinsp;<i>u<sub>3</sub></i><i>K</i><sup>stru</sup>, in which <i>K</i><sup>spec</sup>, <i>K</i><sup>spat</sup>, <i>K</i><sup>stru</sup> are radial basis function (RBF) and <i>u<sub>1</sub></i>&thinsp;+&thinsp;<i>u<sub>2</sub></i>&thinsp;+&thinsp;<i>u<sub>3</sub></i>&thinsp;=&thinsp;1. In the experiment, comparison with three other kernel methods, including the spectral-based, the spectral- and spatial-based and the spectral- and structure-based method, is provided for a panchromatic QuickBird image of a suburban area with a size of 900&thinsp;×&thinsp;900 pixels and spatial resolution of 0.6&thinsp;m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80&thinsp;%, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67&thinsp;% and 74&thinsp;%. What's more, the accuracy of the proposed composite kernel method that jointly uses the spectral, spatial, and structure information is highest among the four methods which is increased to 83&thinsp;%. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.

scholarly journals Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2984
Author(s):  
Gyanendra Prasad Joshi ◽  
Fayadh Alenezi ◽  
Gopalakrishnan Thirumoorthy ◽  
Ashit Kumar Dutta ◽  
Jinsang You
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Data Augmentation ◽  
Land Cover Classification ◽  
Remote Sensing Image ◽  
Environmental Modeling ◽  
Classification Model ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

Research on the Optimal Classification Method for Remote Sensing Image Based on the Gabor-PCA Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 3617-3620
Author(s):  
Jing Hui Yang ◽  
Li Guo Wang ◽  
Jin Xi Qian
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Gabor Filter ◽  
Principal Component ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Spectral Features ◽  
Classification Methods ◽  
Remote Sensing Image Classification ◽  
Core Idea

According to the problem that the traditional remote sensing image classification methods focus only on analyzing the spectral features and have low utilization of the spatial information, a new spatial-spectral classification method is proposed in this paper, its core idea is to combine the spectral features base on the Principal Component Analysis (PCA) algorithm with the spatial features extracted by the Gabor filter. Experiments show that, compared with the traditional classification methods, the proposed method can improve the classification accuracy and the Kappa coefficient, which means to bring better classification and visual effects.

scholarly journals Vision Transformers for Remote Sensing Image Classification

2021 ◽  
Vol 13 (3) ◽  
pp. 516
Author(s):  
Yakoub Bazi ◽  
Laila Bashmal ◽  
Mohamad M. Al Rahhal ◽  
Reham Al Dayil ◽  
Naif Al Ajlan
Keyword(s):  
Remote Sensing ◽  
Language Processing ◽  
Additional Data ◽  
Data Augmentation ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Scene Classification ◽  
Remote Sensing Image Classification ◽  
Augmentation Strategies

In this paper, we propose a remote-sensing scene-classification method based on vision transformers. These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in standard convolutional neural networks (CNNs). Instead, they use multihead attention mechanisms as the main building block to derive long-range contextual relation between pixels in images. In a first step, the images under analysis are divided into patches, then converted to sequence by flattening and embedding. To keep information about the position, embedding position is added to these patches. Then, the resulting sequence is fed to several multihead attention layers for generating the final representation. At the classification stage, the first token sequence is fed to a softmax classification layer. To boost the classification performance, we explore several data augmentation strategies to generate additional data for training. Moreover, we show experimentally that we can compress the network by pruning half of the layers while keeping competing classification accuracies. Experimental results conducted on different remote-sensing image datasets demonstrate the promising capability of the model compared to state-of-the-art methods. Specifically, Vision Transformer obtains an average classification accuracy of 98.49%, 95.86%, 95.56% and 93.83% on Merced, AID, Optimal31 and NWPU datasets, respectively. While the compressed version obtained by removing half of the multihead attention layers yields 97.90%, 94.27%, 95.30% and 93.05%, respectively.

scholarly journals REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

2016 ◽  
Vol XLI-B7 ◽  
pp. 419-422
Author(s):  
Xin Yu ◽  
Zongyong Wen ◽  
Zhaorong Zhu ◽  
Qiang Xia ◽  
Lan Shun
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
High Resolution ◽  
Bayesian Networks ◽  
Image Classification ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Geographical Information ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

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