{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T09:20:46Z","timestamp":1766049646614,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62072360, 62001357, 305 61672131,61901367"],"award-info":[{"award-number":["62072360, 62001357, 305 61672131,61901367"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 \u00d7 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, \u201cInception Score\u201d, \u201cHuman Rank\u201d, and \u201cInference Time\u201d are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.<\/jats:p>","DOI":"10.3390\/rs13101894","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T22:46:14Z","timestamp":1620859574000},"page":"1894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4971-5029","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8975-5773","authenticated-orcid":false,"given":"Hongxiang","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1762-0794","authenticated-orcid":false,"given":"Guorun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Ning","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Hua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid JiLin Province Electric Power Company Limited Information Communication Company, Changchun 130000, China"}]},{"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[{"name":"The School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.1109\/TII.2018.2873492","article-title":"Deep Learning and Superpixel Feature Extraction Based on Contractive Autoencoder for Change Detection in SAR Images","volume":"14","author":"Lv","year":"2018","journal-title":"IEEE Trans. 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