# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
###### Abstract
Their models achieved overall accuracy scores of 92.46%, 94.38%, and 99.64% using Resnet50V2, InceptionV3, and VGG19, respectively. Several studies have explored the use of pre-trained models on remote sensing image classification using CNN-based models [21; 22; 23; 24]. [PERSON] et al. [6] proposed a method that utilizes transferring learning on the high-resolution SAR-labeled land cover labeled dataset [25]. Their method exhibited high efficiency and achieved an overall accuracy of 99.46%. [PERSON] et al. [26] presented a High-Spatial-Resolution Remote Sensing (HSRRS) [27] image scene classification procedure based on transfer learning and the DeCNN (TL-DeCNN) model. Compared to the VGG19 [28], ResNet50 [29], and InceptionV3 [30], their results showed that CNNs with transfer learning can deliver substantially greater accuracy without overfitting [26]. Regarding LULC classification on the EuroSAT dataset [31; 32], a labeled dataset using Sentinel-2 satellite images, [PERSON] et al. implemented a patch-based classification model for LULC classification [31; 33]. They developed a dataset with exactly 27,000 labeled and geo-referenced images, each with 13 bands and divided into ten categories. Based on these images, they examined several band configurations of the GoogleNet [34] and ResNet-50 [29] architectures and discovered that ResNet-50 with RGB bands provided the best accuracy [31]. The EuroSAT dataset was separated into two sets: a train set (80%) for training and a test set (20%) for validation and testing. The overall classification accuracy for the new adaptive dataset was 98.57% [31; 33]. Using the RGB bands, the Deep Discriminative Representation Learning with Attention Map (DDRL-AM) approach was presented by [PERSON] et al. [35]. The authors segregated the EuroSAT dataset into a training set (80%) and a testing set (20%) and achieved an overall accuracy of 98.74% [35]. [PERSON] et al. [36] investigated two methods for enhancing the accuracy of LULC classification using the EuroSAT dataset. The first procedure utilized Sentinel-2's thirteen spectrums for feature extraction, with 98.78% accuracy. In the second technique, thirteen Sentinel-2 spectral feature bands were utilized for feature extraction coupled with computed indices such as Vegetation Index based on the Red Edge (VIRE), Normalized Near-Infrared (NNIR), and Blue Ratio(BR). They divided the Eurosat dataset into a train set (70%), a validation set (20%), and a test set (10%) per class. Their overall accuracy is 99.58% [36]. [PERSON] et al. [12] used the RGB version of the EuroSAT dataset in conjunction with transfer learning. They enhanced performance and processing times by employing techniques such as adaptive learning rates, early stopping, gradient clipping, and data augmentation. They divided the dataset into a train (75%) set and a test (25%) set which was utilized for both validating and testing. Their results indicated that their proposed method based on Wide ResNet-50 reached an overall accuracy of 99.17% [12]. The remainder of this article is organized as follows. Section 2 describes the EuroSAT dataset, deep transfer learning algorithms, data augmentations, EuroSAT dataset subsets for training, validating and evaluating our models, early stopping and learning rate optimizations, the loss and optimizer of models, and accuracy metrics. Section 3 summarizes the key findings of our numerical experiments, including model comparisons through the training time and accuracy metrics. Section 4 discusses the results, and Section 5 concludes this article.