{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:06:09Z","timestamp":1753880769889,"version":"3.41.2"},"reference-count":43,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p> Synthetic Aperture Radar (SAR) represents\u00a0a type of active remote sensing technology that uses microwave electromagnetic radiation\u00a0to produce and send data to the surface of a target location. SAR imaging is frequently used in national security applications since it is unaffected by weather, geographical location, or time. In this system, many approaches are\u00a0examined, to improve automation for segmentation and classification. The utilization of Deep Neural Networks (DNNs) to classify SAR\u00a0images has gotten a lot of attention, and it usually requires several layers of deep models for feature learning. With insufficient training data, however, the DNN\u00a0will get affected by the overfitting issue. The major purpose of this work is to make a development on introducing a new framework for SAR image segmentation and categorization using deep learning. Owing to the coherent nature of the backscattering signal, SARs create speckle noise in their images. If the image has noisy material, classification becomes more challenging. Hence, the pre-processing of the images is employed by linear spatial filtering to remove the noise. Further, the Optimized U-Net is used for the segmentation. For the segmented images, the Binary Robust Independent Elementary Features (BRIEF) concept is adopted as the feature descriptor. These features are inputted to the Convolutional Neural Network (CNN) with Tuned Weight DNN (C-TWDNN) for the classification. In both segmentation and classification, the parameter tuning is employed by the combination of Galactic Swarm Optimization (GSO) and Deer Hunting Optimization Algorithm (DHOA) called the Self-adaptive-Galactic Deer Hunting Optimization (SA-GDHO). Experiments are conducted on a variety of public datasets, demonstrating that our method is capable of outperforming various expert systems and deep structured architectures. <\/jats:p>","DOI":"10.1142\/s0218001422520279","type":"journal-article","created":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T17:21:50Z","timestamp":1669569710000},"source":"Crossref","is-referenced-by-count":3,"title":["Automated SAR Image Segmentation and Classification Using Modified Deep Learning"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5550-3682","authenticated-orcid":false,"given":"G.","family":"Srinitya","sequence":"first","affiliation":[{"name":"Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam 638401, Tamil Nadu, India"}]},{"given":"D.","family":"Sharmila","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore 641407, Tamil Nadu, India"}]},{"given":"S.","family":"Logeswari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, Tamil Nadu, India"}]},{"given":"S. Daniel Madan","family":"Raja","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam 638401, Tamil Nadu, India"}]}],"member":"219","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"issue":"4","key":"S0218001422520279BIB001","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/TGRS.2005.862508","volume":"44","author":"Ainsworth T. L.","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"S0218001422520279BIB002","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1109\/LGRS.2016.2605583","volume":"13","author":"Arisoy S.","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"7","key":"S0218001422520279BIB003","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/LGRS.2017.2702062","volume":"14","author":"Braga A. M.","year":"2017","journal-title":"IEEE Geosci. Remote Sens. 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