High Spatial Resolution Remote Sensing Image Classification Based on Deep Learning

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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Deep Learning Analysis for Big Remote Sensing Image Classification

Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management ◽

10.5220/0008166303550362 ◽

2019 ◽

Author(s):

Imen Chebbi ◽

Nedra Mellouli ◽

Myriam lamolle ◽

Imed Farah

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Image Classification ◽

Remote Sensing Image ◽

Remote Sensing Image Classification ◽

Learning Analysis

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Granular space, knowledge-encoded deep learning architecture and remote sensing image classification

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2020.103647 ◽

2020 ◽

Vol 92 ◽

pp. 103647

Author(s):

Saroj K. Meher

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Image Classification ◽

Remote Sensing Image ◽

Remote Sensing Image Classification

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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/isprs-archives-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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Convolutional Neural Networks for High Spatial Resolution Remote Sensing Image Classification

10.21203/rs.3.rs-953414/v1 ◽

2021 ◽

Author(s):

Rajagopal T K P ◽

Sakthi G ◽

Prakash J

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Image Classification ◽

Convolutional Neural Networks ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Remote Sensing Image ◽

Sensing Data

Abstract Hyperspectral remote sensing based image classification is found to be a very widely used method employed for scene analysis that is from a remote sensing data which is of a high spatial resolution. Classification is a critical task in the processing of remote sensing. On the basis of the fact that there are different materials with reflections in a particular spectral band, all the traditional pixel-wise classifiers both identify and also classify all materials on the basis of their spectral curves (or pixels). Owing to the dimensionality of the remote sensing data of high spatial resolution along with a limited number of labelled samples, a remote sensing image of a high spatial resolution tends to suffer from something known as the Hughes phenomenon which can pose a serious problem. In order to overcome such a small-sample problem, there are several methods of learning like the Support Vector Machine (SVM) along with the other methods that are kernel based and these were introduced recently for a remote sensing classification of the image and this has shown a good performance. For the purpose of this work, an SVM along with Radial Basis Function (RBF) method was proposed. But, a feature learning approach for the classification of the hyperspectral image is based on the Convolutional Neural Networks (CNNs). The results of the experiment that were based on various image datasets that were hyperspectral which implies that the method proposed will be able to achieve a better performance of classification compared to other traditional methods like the SVM and the RBF kernel and also all conventional methods based on deep learning (CNN).

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