Mapping of Intrusive Complex on a Small Scale Using Multi-Source Remote Sensing Images

Zhang, Yuzhou; Zhang, Dengrong; Duan, Jinwei; Hu, Tangao

doi:10.3390/ijgi9090543

Cited by 1 publication

(1 citation statement)

References 48 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By automatically learning and extracting the features of remote sensing images, end-to-end classification learning is carried out, and each pixel in the images is assigned to its own category label. Compared with traditional methods, the depth combination of remote sensing spatial information and model algorithm has made great progress in remote sensing image classification, object detection, image semantic segmentation, and other application directions [5].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Water Body Recognition Algorithm Based on Deep Convolution Generating Network and Combined Features

Zhao

Feng

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The application of remote sensing images in water body recognition has become an effective method for ecological environment detection and evaluation, which has the disadvantages of low efficiency due to the existence of interpretation marks and rich interpretation experience in the current water body environment recognition, and overreliance on human experience. In this paper, the water body recognition method is applied to remote sensing images by combining the deep convolution generation network and the combined features, which has the advantage of high recognition accuracy. In the convolutional neural network, a five-layer convolutional neural network is used to construct a remote sensing water information extraction model, the transfer learning idea is introduced, and the densely connected feature fusion structure is added, so as to achieve the purposes of accelerating the convergence speed of the neural network, reducing the requirements of the neural network on the scale of training data, and reducing the loss of spatial hierarchical information and small object information. Compared with SVM, DBN, and CNN models, the experimental results show that the recognition accuracy of the proposed method is as high as 95. 69% under the constraint of scale window, which has a wide range of application scenarios and practical significance.

show abstract

Section: Introductionmentioning

confidence: 99%