{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:02:40Z","timestamp":1767650560597,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2011,5,2]],"date-time":"2011-05-02T00:00:00Z","timestamp":1304294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H\/A\/\u03b1 decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 \u00d7 10\u22127 s.<\/jats:p>","DOI":"10.3390\/s110504721","type":"journal-article","created":{"date-parts":[[2011,5,4]],"date-time":"2011-05-04T09:03:57Z","timestamp":1304499837000},"page":"4721-4743","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization"],"prefix":"10.3390","volume":"11","author":[{"given":"Yudong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lenan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2011,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1016\/j.pss.2009.07.005","article-title":"Impact of the plasma fluctuations in the Martian ionosphere on the performance of the synthetic aperture ground-penetrating radar","volume":"57","author":"Ilyushin","year":"2009","journal-title":"Planet. 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