Interpretation and Research On Landuse Based On Landsat 7 ETM Plus Remote Sensing Data

Hong, Haoyuan; Xu, Chong; Liu, Ximing; Chen, Wei

doi:10.1088/1755-1315/44/3/032003

Cited by 1 publication

(1 citation statement)

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“…In general, pixel classification algorithms can be divided into unsupervised classification and supervised classification methods. Unsupervised classification does not require prior knowledge (Puletti et al, 2014), and corresponding methods include the k-means (Blanzieri and Melgani, 2008) algorithm and ISODATA clustering algorithm (Hong et al, 2016). However, unsupervised classification requires the postprocessing and analysis of the classified results.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing image classification based on object-oriented convolutional neural network

et al. 2022

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Remote sensing image classification is of great importance for urban development and planning. The need for higher classification accuracy has led to improvements in classification technology. In this research, Landsat 8 images are used as experimental data, and Wuhan, Chengde and Tongchuan are selected as research areas. The best neighborhood window size of the image patch and band combination method are selected based on two sets of comparison experiments. Then, an object-oriented convolutional neural network (OCNN) is used as a classifier. The experimental results show that the classification accuracy of the OCNN classifier is 6% higher than that of an SVM classifier and 5% higher than that of a convolutional neural network classifier. The graph of the classification results of the OCNN is more continuous than the plots obtained with the other two classifiers, and there are few fragmentations observed for most of the category. The OCNN successfully solves the salt and pepper problem and improves the classification accuracy to some extent, which verifies the effectiveness of the proposed object-oriented model.

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Section: Introductionmentioning

confidence: 99%