## 4 Experimental Validation  
The performance validation of the proposed model is carried out using the UCM dataset [24]. The dataset contains a total of 2100 images and 21 classes (agricultural, airplane, baseballdiamond, beach, buildings, chaparral, denseresidential, forest, freeway, golfcourse, harbor, intersection, mediumresidential, mobilehomerak, overpass, parkinglot, river, runway, sparse residential, storagetanks, and tenniscourt). It includes a total of 100 images under each class. The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. The pixel resolution of this public domain imagery is 1 foot. Each image measures 256 \(\times\) 256 pixels. For experimental validation, the dataset is split into 70% of training set and 30% of testing set, i.e., 70 images from each class for training and remaining 30 images for testing purposes. Figure 3 showcases the sample images of UCM dataset. Figure 4 illustrates the confusion matrices provided by the SIDTLD-AIC model on 70% of UCM datasets as training datasets. The results indicated that the SIDTLD-AIC model has effectually categorized all the 21 classes. Figure 3: Samples-UCM Dataset. Table 1 reports the overall classification outcomes of the SIDTLD-AIC model 70% of UCM datasets as training datasets. The results inferred that the SIDTLD-AIC model has accomplished enhanced classifier outcomes on all class labels. For instance, the SIDTLD-AIC model has recognized class 1 samples with \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{mean}\) of 99.39%, 90.41%, 97.06%, 93.62%, and 98.27%, respectively. Along with that, the SIDTLD-AIC method has recognized class 3 samples with \(accu_{y}\), \(prec_{n}\), \(rec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{mean}\) of 99.66%, 98.53%, 94.37%, 96.40%, and 97.11%, correspondingly. Moreover, the SIDTLD-AIC system has recognized class 13 samples with \(accu_{y}\), \(prec_{n}\), \(rec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{mean}\) of 99.32%, 91.67%, 94.29%, 92.96%, and 96.89%, correspondingly. Furthermore, the SIDTLD-AIC approach has recognized class 16 samples with \(accu_{y}\), \(prec_{n}\), \(rec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{mean}\) of 99.66%, 97.10%, 95.71%, 96.40%, and 97.76%, respectively. Lastly, the SIDTLD-AIC method has recognized class 20 samples with \(accu_{y}\), \(prec_{n}\), \(rec_{d}\), \(F_{score}\), and \(G_{mean}\) of 99.73%, 95.71%, 98.53%, 97.10%, and 99.16%, correspondingly. Figure 4: Confusion matrix of SIDTLD-AIC technique on 70% of UCM datasets as training datasets. Figure 5 showcases the confusion matrices provided by the SIDTLD-AIC approach on 30% of UCM datasets as testing datasets. The results point out that the SIDTLD-AIC methodology has effectually categorized all the 21 classes. Table 2 demonstrates the overall classification outcomes of the SIDTLD-AIC method on 30% of UCM datasets as testing datasets. The results exposed that the SIDTLD-AIC model has accomplished higher classifier outcomes on all class labels. For instance, the SIDTLD-AIC method has recognized class 1 samples with \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(F_{score}\), and \(G_{mean}\) of 99.68%, 100%, 93.75%, 96.77%, and 96.82%, respectively. Next, the SIDTLD-AIC model has recognized class 3 samples with \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(F_{score}\), and \(G_{mean}\) of 99.52%, 96.43%, 93.10%, 94.74%, and 96.41%, correspondingly. Furthermore, the SIDTLD-AIC system has recognized class 13 samples with \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(F_{score}\), and \(G_{mean}\) of 99.21%, 93.10%, 90%, 91.53%, and 94.71%, respectively. Moreover, the SIDTLD-AIC methodology has recognized class 16 samples with \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), \(F_{score}\), and \(G_{mean}\) of 99.52%, 100%, 90%, 94.74%, and 94.87%, respectively.