Auto‐encoder‐based shared mid‐level visual dictionary learning for scene classification using very high resolution remote sensing images

Effective representation and classification of scenes using very high resolution (VHR) remote sensing images cover a wide range of applications. Although robust low‐level image features have been proven to be effective for scene classification, they are not semantically meaningful and thus have difficulty to deal with challenging visual recognition tasks. In this study, the authors propose a new and effective auto‐encoder‐based method to learn a shared mid‐level visual dictionary. This dictionary serves as a shared and universal basis to discover mid‐level visual elements. On the one hand, the mid‐level visual dictionary learnt using machine learning technique is more discriminative and contains rich semantic information, compared with the traditional low‐level visual words. On the other hand, the mid‐level visual dictionary is more robust to occlusions and image clutters. In the authors' scene‐classification scheme, they use discriminative mid‐level visual elements, rather than individual pixels or low‐level image features, to represent images. This new image representation is able to capture much of the high‐level meaning and contents of the image, facilitating challenging remote sensing image scene‐classification tasks. Comprehensive evaluations on a challenging VHR remote sensing images data set and comparisons with state‐of‐the‐art approaches demonstrate the effectiveness and superiority of their study

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.Comment: This manuscript is the accepted version for Proceedings of the IEE

Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines