Abstract
Image classification has gained lot of attention due to its application in different computer vision tasks such as remote sensing, scene analysis, surveillance, object detection, and image retrieval. The primary goal of image classification is to assign the class labels to images according to the image contents. The applications of image classification and image analysis in remote sensing are important as they are used in various applied domains such as military and civil fields. Earlier approaches for remote sensing images and scene analysis are based on low-level feature representations such as color- and texture-based features. Vector of Locally Aggregated Descriptors (VLAD) and orderless Bag-of-Features (BoF) representations are the examples of mid-level approaches for remote sensing image classification. Recent trends for remote sensing and scene classification are focused on the use of Convolutional Neural Network (CNN). Keeping in view the success of CNN models, in this research, we aim to fine-tune ResNet50 by using network surgery and creation of network head along with the fine-tuning of hyperparameters. The learning of hyperparameters is tuned by using a linear decay learning rate scheduler known as piecewise scheduler. To tune the optimizer hyperparameter, Stochastic Gradient Descent with Momentum (SGDM) is used with the usage of weight learn and bias learn rate factor. Experiments and analysis are conducted on five different datasets, that is, UC Merced Land Use Dataset (UCM), RSSCN (the remote sensing scene classification image dataset), SIRI-WHU, Corel-1K, and Corel-1.5K. The analysis and competitive results exemplify that our proposed image classification-based model can classify the images in a more effective and efficient manner as compared to the state-of-the-art research.
Highlights
Image classification and analysis is an active research area and there are many applications of automatic image classification in computer vision domains such as pattern recognition, image retrieval, object recognition, remote sensing, face recognition, textile image analysis, automatic disease detection, geographic mapping, and video processing [1,2,3]
It can be seen that the proposed research achieves highest classification accuracy as compared to the methods based on deep learning models, that is, AlexNet, GoogLeNet, Inception-V3, VGGVD-16, and CaffeNet, outperforming these methods by 6.4%, 6.16%, 5%, 4.82%, and 3.75%, respectively
In [43], a hybrid feature vector is created by integrating three visual attributes, that is, color, texture, and shape. e experimental evaluation and analysis illustrate that the implemented technique outstrips many state-of-the-art related approaches based on varied hybrid systems. e proposed research achieves the highest accuracy as compared to the state-of-the-art research, thereby outperforming the researches of Li et al [61], Aslam et al [14], SCNN-Extreme Learning Machine (ELM) [61], MKSVM-MIL et al [62], Raja et al [41], Desai et al [42], Yu et al [44], and Shikha et al [43] by 26.16%, 15.74%, 12.68%, 11.8%, 10.34%, 8.8%, 1.02%, and 0.5%, respectively
Summary
Image classification and analysis is an active research area and there are many applications of automatic image classification in computer vision domains such as pattern recognition, image retrieval, object recognition, remote sensing, face recognition, textile image analysis, automatic disease detection, geographic mapping, and video processing [1,2,3]. In any image classification-based model, the primary objective of research is to assign the class labels to images. A group of images are used as training samples and learning of classification-based model is done by using a training dataset. The test dataset is assigned to the trained model to predict the class labels of images. On the basis of prediction of test dataset, images can be arranged in a semantic and meaningful order. The problem of image classification is more challenging as objects are rotated within a view and background is usually more complex [7]. Satellites, unmanned aerial vehicles, and aerial systems are used to capture the image datasets that are used to evaluate the research of remote sensing [7].
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