2020 |
Voulodimos, Athanasios; Fokeas, K; Doulamis, Nikolaos; Doulamis, Anastasios; Makantasis, K NOISE-TOLERANT HYPERSPECTRAL IMAGE CLASSIFICATION USING DISCRETE COSINE TRANSFORM AND CONVOLUTIONAL NEURAL NETWORKS Journal Article In: ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLIII-B2-2 , pp. 1281–1287, 2020, ISSN: 2194-9034. Abstract | Links | BibTeX | Tags: Convolutional Neural Networks, deep learning, Discrete Cosine Transform, Hyperspectral image classification, noise tolerance, robustness to noise @article{Voulodimos2020,Abstract. Hyperspectral image classification has drawn significant attention in the recent years driven by the increasing abundance of sensor-generated hyper- and multi-spectral data, combined with the rapid advancements in the field of machine learning. A vast range of techniques, especially involving deep learning models, have been proposed attaining high levels of classification accuracy. However, many of these approaches significantly deteriorate in performance in the presence of noise in the hyperspectral data. In this paper, we propose a new model that effectively addresses the challenge of noise residing in hyperspectral images. The proposed model, which will be called DCT-CNN, combines the representational power of Convolutional Neural Networks with the noise elimination capabilities introduced by frequency-domain filtering enabled through the Discrete Cosine Transform. In particular, the proposed method entails the transformation of pixel macroblocks to the frequency domain and the discarding of information that corresponds to the higher frequencies in every patch, in which pixel information of abrupt changes and noise often resides. Experiment results in Indian Pines, Salinas and Pavia University datasets indicate that the proposed DCT-CNN constitutes a promising new model for accurate hyperspectral image classification offering robustness to different types of noise, such as Gaussian and salt and pepper noise. |
Bakalos, Nikolaos; Soile, Sofia; Ioannidis, Charalabos In: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Association for Computing Machinery, Corfu, Greece, 2020, ISBN: 9781450377737. Abstract | Links | BibTeX | Tags: complex surfaces, Convolutional Neural Networks, Cultural heritage, material detection, semantic classification @inproceedings{10.1145/3389189.3398001,https://doi.org/10.1145/3389189.3398000 Historic preservation of tangible cultural heritage assets is a process that goes beyond structural integrity to the restoration of the interior decorations, such as wall-paintings or icons since this provides a complete restoration process of the monuments that face both their architectural and functional elements. This process is imperative, as in a lot of cases parts of the assets (e.g., frescoes) are decayed or missing due to the passage of time and other environmental, natural or anthropogenic factors. An indicative paradigm of such a decay is the Byzantine churches in Meteora area, a UNESCO cultural heritage site in Greece. However, the limitations in taking samples from such sights indicate that before such fresco restoration process commences, we first need to semantically classify the monument surfaces into different material types, such as stone, mortar or frescoes. The research challenge imposes this semantic classification process is more evident in cases where the surfaces of the monument are not planar but complex, such as in many byzantine churches carved in rock in Meteora.In this paper, the semantic classification is achieved using a deep Convolutional Neural Network (CNN) which receives as input two types of data: RGB images of the frescoes to capture textural information and 3D cubes that encapsulate the geometric structure of the surface. RGB images describe visual complexity of the frescoes including texture maps and style. On the other hand, the 3D cubes include triangles of the surface, obtained using photogrammetric methods, describing surface complexity. The CNN consist of two layers; a deep convolutional layer which automatically extracts a set of reliable features from the input raw data and a conventional feedforward neural-based classification layer. To detect the missing items and the material types, overlapped input data are fed as inputs to the CNN as if the network "scan" the decorations to discriminate the type of their materials. The classification performance is tested on real-world destroyed byzantine frescoes of Saint Stephanus Monastery in Meteora. |
2020 |
NOISE-TOLERANT HYPERSPECTRAL IMAGE CLASSIFICATION USING DISCRETE COSINE TRANSFORM AND CONVOLUTIONAL NEURAL NETWORKS Journal Article In: ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLIII-B2-2 , pp. 1281–1287, 2020, ISSN: 2194-9034. |
In: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Association for Computing Machinery, Corfu, Greece, 2020, ISBN: 9781450377737. |