{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:38:33Z","timestamp":1771274313226,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) analysis generally suffers from issues such as high dimensionality, imbalanced sample sets for different classes, and the choice of classifiers for artificially balanced datasets. The existing conventional data imbalance removal techniques and forest classifiers lack a more efficient approach to dealing with the aforementioned issues. In this study, we propose a novel hybrid methodology ADASYN-enhanced subsampled multi-grained cascade forest (ADA-Es-gcForest) which comprises four folds: First, we extracted the most discriminative global spectral features by reducing the vast dimensions, i.e., the redundant bands using principal component analysis (PCA). Second, we applied the subsampling-based adaptive synthetic minority oversampling method (ADASYN) to augment and balance the dataset. Third, we used the subsampled multi-grained scanning (Mg-sc) to extract the minute local spatial\u2013spectral features by adaptively creating windows of various sizes. Here, we used two different forests\u2014a random forest (RF) and a complete random forest (CRF)\u2014to generate the input joint-feature vectors of different dimensions. Finally, for classification, we used the enhanced deep cascaded forest (CF) that improvised in the dimension reduction of the feature vectors and increased the connectivity of the information exchange between the forests at the different levels, which elevated the classifier model\u2019s accuracy in predicting the exact class labels. Furthermore, the experiments were accomplished by collecting the three most appropriate, publicly available his landcover datasets\u2014the Indian Pines (IP), Salinas Valley (SV), and Pavia University (PU). The proposed method achieved 91.47%, 98.76%, and 94.19% average accuracy scores for IP, SV, and PU datasets. The validity of the proposed methodology was testified against the contemporary state-of-the-art eminent tree-based ensembled methods, namely, RF, rotation forest (RoF), bagging, AdaBoost, extreme gradient boost, and deep multi-grained cascade forest (DgcForest), by simulating it numerically. Our proposed model achieved correspondingly higher accuracies than those classifiers taken for comparison for all the HS datasets.<\/jats:p>","DOI":"10.3390\/rs14194853","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T22:53:19Z","timestamp":1664405599000},"page":"4853","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Hybrid Classification of Imbalanced Hyperspectral Images Using ADASYN and Enhanced Deep Subsampled Multi-Grained Cascaded Forest"],"prefix":"10.3390","volume":"14","author":[{"given":"Debaleena","family":"Datta","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar 751024, Odisha, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-0757","authenticated-orcid":false,"given":"Pradeep Kumar","family":"Mallick","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar 751024, Odisha, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3345-651X","authenticated-orcid":false,"given":"Annapareddy V. N.","family":"Reddy","sequence":"additional","affiliation":[{"name":"Lakireddy Bali Reddy College of Engineering, Mylavaram 521230, Andhra Predesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9030-8102","authenticated-orcid":false,"given":"Mazin Abed","family":"Mohammed","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq"}]},{"given":"Mustafa Musa","family":"Jaber","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Dijlah University College, Baghdad 00964, Iraq"},{"name":"Department of Computer Science, Al-Turath University College, Baghdad 10021, Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-2141","authenticated-orcid":false,"given":"Abed Saif","family":"Alghawli","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-7641","authenticated-orcid":false,"given":"Mohammed A. A.","family":"Al-qaness","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China"},{"name":"Faculty of Engineering, Sana\u2019a University, Sana\u2019a 12544, Yemen"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Han, Y., Li, J., Zhang, Y., Hong, Z., and Wang, J. (2017). Sea ice detection based on an improved similarity measurement method using hyperspectral data. Sensors, 17.","DOI":"10.3390\/s17051124"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jspr.2015.01.006","article-title":"Hyperspectral imaging to classify and monitor quality of agricultural materials","volume":"61","author":"Mahesh","year":"2015","journal-title":"J. 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