scholarly journals SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 681-685
Author(s):  
Y. Chen ◽  
D. Ming
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Classification Accuracy ◽  
High Spatial Resolution ◽  
Scale Parameter ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Scale ◽  
Digital Orthophoto

<p><strong>Abstract.</strong> In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image. Per-pixel classification method based on CNN (Per-pixel CNN) achieved higher accuracy with the help of high-level features, however, this method still has limitations. Even though per-superpixel classification method based on CNN (Per-superpixel CNN) overcome the limitations of per-pixel CNN, classification accuracy of complex urban is easily influenced by scale effect. To solve this issue, superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) method is proposed. Besides, this paper proposes a novel spatial statistics based method to estimate applicable scale parameter of per-superpixel CNN. Experiments using proposed method were performed on Digital Orthophoto Quarer Quad (DOQQ) images in urban and suburban area. Classification results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent.</p>

scholarly journals Convolutional Neural Networks for High Spatial Resolution Remote Sensing Image Classification

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).

scholarly journals High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Meimei Duan ◽  
Lijuan Duan
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Spectrum Sharing ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Data Classification ◽  
Remote Sensing Image ◽  
Classification Model ◽  
Classification Method ◽  
Sensing Data

Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial resolution remote sensing data and correct the pixel brightness of the fused multispectral image. The initial data are normalized, the feature of spectral image is extracted, the convolution neural network classification model is constructed, and the remote sensing image is segmented. Experimental results show that the proposed method takes shorter time and has higher accuracy for high spatial resolution image segmentation. High spatial resolution remote sensing data classification is more efficient, and the accuracy of data classification and remote sensing image fusion are more ideal.

scholarly journals Purifying SLIC Superpixels to Optimize Superpixel-Based Classification of High Spatial Resolution Remote Sensing Image

2019 ◽  
Vol 11 (22) ◽  
pp. 2627 ◽  
Author(s):  
Hengjian Tong ◽  
Fei Tong ◽  
Wei Zhou ◽  
Yun Zhang
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Color Quantization ◽  
Single Class ◽  
Segmentation Evaluation ◽  
Ground Object ◽  
Accuracy Increase

Fast and accurate classification of high spatial resolution remote sensing image is important for many applications. The usage of superpixels in classification has been proposed to accelerate the speed of classification. However, although most superpixels only contain pixels from single class, there are still some mixed superpixels, which mostly locate near the edge of different classes, and contain pixels from more than one class. Such mixed superpixels will cause misclassification regardless of classification methods used. In this paper, a superpixels purification algorithm based on color quantization is proposed to purify mixed Simple Linear Iterative Clustering (SLIC) superpixels. After purifying, the mixed SLIC superpixel will be separated into smaller superpixels. These smaller superpixels are pure superpixels which only contain a single kind of ground object. The experiments on images from the dataset BSDS500 show that the purified SLIC superpixels outperform the original SLIC superpixels on three segmentation evaluation metrics. With the purified SLIC superpixels, a classification scheme in which only edge superpixels are selected to be purified is proposed. The strategy of purifying edge superpixels not only improves the efficiency of the algorithm, but also improves the accuracy of the classification. The experiments on a remote sensing image from WorldView-2 satellite demonstrate that purified SLIC superpixels at all scales can generate classification result with higher accuracy than original SLIC superpixels, especially at the scale of 20 × 20 , for which the accuracy increase is higher than 4%.

Object-Based Multi-Feature Classification of High Spatial Resolution Remote Sensing Image

2015 ◽  
Vol 733 ◽  
pp. 935-938
Author(s):  
Xian De Huang ◽  
Heng Chu ◽  
Ru Yan Wang
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Paper Analysis ◽  
Image Feature ◽  
Original Image ◽  
Feature Classification ◽  
Object Based

This paper analysis the characteristics of High Spatial Resolution Remote Sensing Image (HSRRSI), consider the drawback of pixel-wise Classification, an Object-based method was taken to classify the image. The paper firstly preprocess and coarsely segment the image, secondly optimize the segmented results for deeply processing, then extracting the image feature from the original image and segmented results, finally classify the image and assess the results.

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