30 research outputs found
DALF: An AI Enabled Adversarial Framework for Classification of Hyperspectral Images
Hyperspectral image classification is very complex and challenging process. However, with deep neural networks like Convolutional Neural Networks (CNN) with explicit dimensionality reduction, the capability of classifier is greatly increased. However, there is still problem with sufficient training samples. In this paper, we overcome this problem by proposing an Artificial Intelligence (AI) based framework named Deep Adversarial Learning Framework (DALF) that exploits deep autoencoder for dimensionality reduction, Generative Adversarial Network (GAN) for generating new Hyperspectral Imaging (HSI) samples that are to be verified by a discriminator in a non-cooperative game setting besides using aclassifier. Convolutional Neural Network (CNN) is used for both generator and discriminator while classifier role is played by Support Vector Machine (SVM) and Neural Network (NN). An algorithm named Generative Model based Hybrid Approach for HSI Classification (GMHA-HSIC) which drives the functionality of the proposed framework is proposed. The success of DALF in accurate classification is largely dependent on the synthesis and labelling of spectra on regular basis. The synthetic samples made with an iterative process and being verified by discriminator result in useful spectra. By training GAN with associated deep learning models, the framework leverages classification performance. Our experimental results revealed that the proposed framework has potential to improve the state of the art besides having an effective data augmentation strategy
State-of-the-art and gaps for deep learning on limited training data in remote sensing
Deep learning usually requires big data, with respect to both volume and
variety. However, most remote sensing applications only have limited training
data, of which a small subset is labeled. Herein, we review three
state-of-the-art approaches in deep learning to combat this challenge. The
first topic is transfer learning, in which some aspects of one domain, e.g.,
features, are transferred to another domain. The next is unsupervised learning,
e.g., autoencoders, which operate on unlabeled data. The last is generative
adversarial networks, which can generate realistic looking data that can fool
the likes of both a deep learning network and human. The aim of this article is
to raise awareness of this dilemma, to direct the reader to existing work and
to highlight current gaps that need solving.Comment: arXiv admin note: text overlap with arXiv:1709.0030
Formation of an informative index for recognizing specified objects in hyperspectral data
Π Π°Π±ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅ΠΌΡ ΠΏΠΎ ΠΌΠ°Π»ΠΎΠΌΡ ΡΠΈΡΠ»Ρ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ ΠΏΡΠ°Π²ΠΈΠ»Π° ΡΠ°Π·Π»ΠΈΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π³ΠΈΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
. Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΏΠΎΠ΄ΠΎΠ±Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΠΎΠ²Π°Π»Π° Π±Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² Π΄Π»Ρ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π³ΠΈΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡΡ
ΠΊΠ°ΠΊ Π΄Π»Ρ ΠΏΡΠ΅Π΄Π²Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ, ΡΠ°ΠΊ ΠΈ Π΄Π»Ρ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ ΡΠ°Π·ΠΌΠ΅ΡΠΊΠΈ Π³ΠΈΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
. ΠΠ»Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡ, Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΡΡΡΡ Π² ΡΠΎΠ²ΠΌΠ΅ΡΡΠ½ΠΎΠΌ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΠ±ΡΠΈΡ
ΠΏΡΠ°Π²ΠΈΠ» Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ² ΠΈ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΠΈ. Π ΡΠ°ΠΌΠΊΠ°Ρ
Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΠΏΡΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠΉ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈΠ½Π΄Π΅ΠΊΡ Π·Π°Π΄Π°Π΅ΡΡΡ Π½ΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠ°Π·Π½ΠΎΡΡΠ½ΠΎΠΉ ΡΠΎΡΠΌΡΠ»ΠΎΠΉ, Π° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΡ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π΅ΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΡΠ°Π·Π΄Π΅Π»ΠΈΠΌΠΎΡΡΠΈ Π΄ΠΈΡΠΊΡΠΈΠΌΠΈΠ½Π°Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΏΡΠΎΠ²Π΅Π΄ΡΠ½Π½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, Π±ΡΠ»ΠΎ ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°, ΡΠ΅Π°Π»ΠΈΠ·ΡΡΡΠ΅Π³ΠΎ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡ, Π±ΡΠ»Π° ΡΠ΅ΡΠ΅Π½Π° Π·Π°Π΄Π°ΡΠ° ΡΠ°Π·Π»ΠΈΡΠ΅Π½ΠΈΡ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ Π³ΠΈΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
Ρ ΡΠ°Π·Π½ΠΎΠΉ ΡΠ°ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡΡ. Π‘ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠΌ ΠΈΠ½Π΄Π΅ΠΊΡ ΠΎΠΊΠ°Π·Π°Π»ΡΡ Π±Π»ΠΈΠ·ΠΊΠΈΠΌ ΠΏΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΡΠΌ ΠΊ NDVI. ΠΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΠ°Ρ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π³Π΅Π½Π΅ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠ°Π²ΠΈΠ» Π°Π½Π°Π»ΠΈΠ·Π° Π³ΠΈΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΌΠ°Π»ΠΎΠΌΡ ΡΠΈΡΠ»Ρ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ², ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΡ
Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π·Π°Π΄Π°ΡΠ°Ρ
.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ ΠΏΡΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π·Π°Π΄Π°Π½ΠΈΡ ΠΠΈΠ½ΠΎΠ±ΡΠ½Π°ΡΠΊΠΈ Π ΠΎΡΡΠΈΠΈ Π‘Π°ΠΌΠ°ΡΡΠΊΠΎΠΌΡ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΡ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠ°Π±ΠΎΡ ΠΠΠ-602 "Π€ΠΎΡΠΎΠ½ΠΈΠΊΠ° Π΄Π»Ρ ΡΠΌΠ½ΠΎΠ³ΠΎ Π΄ΠΎΠΌΠ° ΠΈ ΡΠΌΠ½ΠΎΠ³ΠΎ Π³ΠΎΡΠΎΠ΄Π°" ΡΠ΅ΠΌΠ° 19Π²-Π 001-602 43/21Π (ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π°Ρ ΡΠ°ΡΡΡ), Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΏΡΠΎΠ΅ΠΊΡΠ° β 0777-2020-0017 (ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ), ΠΏΡΠΈ ΡΠ°ΡΡΠΈΡΠ½ΠΎΠΉ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π Π€Π€Π Π² ΡΠ°ΠΌΠΊΠ°Ρ
Π½Π°ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° β 20-51-05008 (ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΡΠ°ΡΡΡ)
A Social Network Image Classification Algorithm Based on Multimodal Deep Learning
The complex data structure and massive image data of social networks pose a huge challenge to the mining of associations between social information. For accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning. Firstly, a social network association clustering model (SNACM) was established, and used to calculate trust and similarity, which represent the degree of similarity between users. Based on artificial ant colony algorithm, the SNACM was subject to weighted stacking, and the social network image association network was constructed. After that, the social network images of three modes, i.e. RGB (red-green-blue) image, grayscale image, and depth image, were fused. Finally, a three-dimensional neural network (3D NN) was constructed to extract the features of the multimodal social network image. The proposed algorithm was proved valid and accurate through experiments. The research results provide a reference for applying multimodal deep learning to classify the images in other fields
Generative adversarial deep learning in images using Nash equilibrium game theory
A generative adversarial learning (GAL) algorithm is presented to overcome the manipulations that take place in adversarial data and to result in a secured convolutional neural network (CNN). The main objective of the generative algorithm is to make some changes to initial data with positive and negative class labels in testing, hence the CNN results in misclassified data. An adversarial algorithm is used to manipulate the input data that represents the boundaries of learnerβs decision-making process. The algorithm generates adversarial modifications to the test dataset using a multiplayer stochastic game approach, without learning how to manipulate the data during training. Then the manipulated data is passed through a CNN for evaluation. The multi-player game consists of an interaction between adversaries which generates manipulations and retrains the model by the learner. The Nash equilibrium game theory (NEGT) is applied to Canadian Institute for Advance Research (CIFAR) dataset. This was done to produce a secure CNN output that is more robust to adversarial data manipulations. The experimental results show that proposed NEGT-GAL achieved a grater mean value of 7.92 and takes less wall clock time of 25,243 sec. Therefore, the proposed NEGT-GAL outperforms the compared existing methods and achieves greater performance
Hybrid deep neural network for Bangla automated image descriptor
Automated image to text generation is a computationally challenging computer vision task which requires sufficient comprehension of both syntactic and semantic meaning of an image to generate a meaningful description. Until recent times, it has been studied to a limited scope due to the lack of visual-descriptor dataset and functional models to capture intrinsic complexities involving features of an image. In this study, a novel dataset was constructed by generating Bangla textual descriptor from visual input, called Bangla Natural Language Image to Text (BNLIT), incorporating 100 classes with annotation. A deep neural network-based image captioning model was proposed to generate image description. The model employs Convolutional Neural Network (CNN) to classify the whole dataset, while Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) capture the sequential semantic representation of text-based sentences and generate pertinent description based on the modular complexities of an image. When tested on the new dataset, the model accomplishes significant enhancement of centrality execution for image semantic recovery assignment. For the experiment of that task, we implemented a hybrid image captioning model, which achieved a remarkable result for a new self-made dataset, and that task was new for the Bangladesh perspective. In brief, the model provided benchmark precision in the characteristic Bangla syntax reconstruction and comprehensive numerical analysis of the model execution results on the dataset
Virtual Hyperspectral Images Using Symmetric Autoencoders
Spectral data acquired through remote sensing are invaluable for
environmental and resource studies. However, these datasets are often marred by
nuisance phenomena such as atmospheric interference and other complexities,
which pose significant challenges for accurate analysis. We show that an
autoencoder architecture, called symmetric autoencoder (SymAE), which leverages
symmetry under reordering of the pixels, can learn to disentangle the influence
of these nuisance from surface reflectance features on a pixel-by-pixel basis.
The disentanglement provides an alternative to atmospheric correction, without
relying on radiative transfer modelling, through a purely data-driven process.
More importantly, SymAE can generate virtual hyperspectral images by
manipulating the nuisance effects of each pixel. We demonstrate using AVIRIS
instrument data that these virtual images are valuable for subsequent image
analysis tasks. We also show SymAE's ability to extract intra-class invariant
features, which is very useful in clustering and classification tasks,
delivering state-of-the-art classification performance for a purely spectral
method