Category Level Object Discovery Using Dynamic Topic Model

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 K = u1Kspec + u2Kspat + u3Kstru, in which Kspec, Kspat, Kstru are radial basis function (RBF) and u1 + u2 + u3 = 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 × 900 pixels and spatial resolution of 0.6 m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67 % and 74 %. 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 %. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-399-2016 ◽

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 K = u1Kspec + u2Kspat + u3Kstru, in which Kspec, Kspat, Kstru are radial basis function (RBF) and u1 + u2 + u3 = 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 × 900 pixels and spatial resolution of 0.6 m. The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based which accuracy respectively is 67 % and 74 %. 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 %. On the other hand, the result of the experiment also verifies the validity of the expression of structure information about the remote sensing image.

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Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning

Earth Sciences Research Journal ◽

10.15446/esrj.v24n3.89750 ◽

2020 ◽

Vol 24 (3) ◽

pp. 357-365

Author(s):

Shujun Liang ◽

Jing Cheng ◽

Jianwei Zhang

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Image Classification ◽

Learning Algorithm ◽

Likelihood Estimation ◽

Remote Sensing Image ◽

Maximum Likelihood Classification ◽

Remote Sensing Image Classification

Soil remote sensing image classification is the most difficult in the National Soil Census work. Current soil remote sensing image classification methods based on deep learning and maximum likelihood estimation are challenging to meet the actual needs. Therefore, this paper combines deep learning with maximum likelihood estimation and proposes a maximum likelihood classification method for soil remote sensing images based on deep learning. The method is divided into four parts. Firstly, the pretreatment of soil remote sensing image is carried out, including three processes: image gray, image denoising, and image correction; secondly, the target of soil remote sensing image is detected by deep learning algorithm; thirdly, the maximum likelihood algorithm is used to classify soil remote sensing image; finally, the classification performance is tested by an example. The results show that this method can effectively segment the remote sensing image of soil, and the segmentation accuracy is high, which proves the effectiveness and superiority of the method.

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Deep Learning Based High-Resolution Remote Sensing Image classification

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.384 ◽

2017 ◽

Vol 7 (10) ◽

pp. 22

Author(s):

Sumit Kaur

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Deep Learning ◽

Image Classification ◽

Language Processing ◽

Object Perception ◽

Remote Sensing Image ◽

Research Area ◽

Remote Sensing Image Classification ◽

Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification.

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

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3454009 ◽

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.

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