For satellite image processing using deep learning, the images are first resized to one standard size and then applied to the convolutional neural network(CNN) model to train and test the model. The primary step of preprocessing the images is resizing, which is done by various approaches such as bicubic interpolation, bilinear interpolation, and nearest neighbour interpolation. The impact of resizing technique on the performance of the deep learning model is unexplored yet. In the proposed work, the authors have proposed a CNN architecture and investigated the impact of resizing technique on the performance of trained deep learning model and evaluating the uncertainty of classification. The classification performed by the proposed model is a multilabel classification problem with 6 possible labels denoting land coverage. The proposed model is trained using SAT-6 dataset images. The SAT-6 dataset contains images of size \(28 \times 28\), which are resized to \(56 \times 56\) using nearest neighbor interpolation for the training of the model, and testing is done using the aforementioned three interpolation techniques. Finally, the model performance is evaluated in term of specificity, sensitivity, accuracy, and uncertainty of the classification. This paper’s contribution is twofold – first, to put forward a generic model that will help other remote-sensing applications, and second to evaluate the uncertainty of the classification task by varying the image resizing technique.

对于使用深度学习的卫星图像处理，首先将图像调整为一个标准尺寸，然后应用于卷积神经网络 (CNN) 模型以训练和测试模型。预处理图像的主要步骤是调整大小，这是通过各种方法完成的，例如双三次插值、双线性插值和最近邻插值。调整大小技术对深度学习模型性能的影响尚未探索。在拟议的工作中，作者提出了一种 CNN 架构，并研究了调整大小技术对训练有素的深度学习模型的性能的影响，并评估了分类的不确定性。所提出的模型执行的分类是一个多标签分类问题，有 6 个可能的标签表示土地覆盖。使用SAT-6数据集图像对提出的模型进行训练。SAT-6 数据集包含大小的图像\(28 \times 28\)，使用最近邻插值将其调整为\(56 \times 56\)用于模型训练，并使用上述三种插值技术完成测试。最后，根据分类的特异性、敏感性、准确性和不确定性来评估模型性能。本文的贡献是双重的——首先，提出一个有助于其他遥感应用的通用模型，其次通过改变图像大小调整技术来评估分类任务的不确定性。