5,044 research outputs found
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study
A Hyperspectral image contains much more number of channels as compared to a
RGB image, hence containing more information about entities within the image.
The convolutional neural network (CNN) and the Multi-Layer Perceptron (MLP)
have been proven to be an effective method of image classification. However,
they suffer from the issues of long training time and requirement of large
amounts of the labeled data, to achieve the expected outcome. These issues
become more complex while dealing with hyperspectral images. To decrease the
training time and reduce the dependence on large labeled dataset, we propose
using the method of transfer learning. The hyperspectral dataset is
preprocessed to a lower dimension using PCA, then deep learning models are
applied to it for the purpose of classification. The features learned by this
model are then used by the transfer learning model to solve a new
classification problem on an unseen dataset. A detailed comparison of CNN and
multiple MLP architectural models is performed, to determine an optimum
architecture that suits best the objective. The results show that the scaling
of layers not always leads to increase in accuracy but often leads to
overfitting, and also an increase in the training time.The training time is
reduced to greater extent by applying the transfer learning approach rather
than just approaching the problem by directly training a new model on large
datasets, without much affecting the accuracy
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Multi-Scale Hybrid Spectral Network for Feature Learning and Hyperspectral Image Classification
Hyperspectral image (HSI) classification is an important concern in remote sensing, but it is complex since few numbers of labelled training samples and the high-dimensional space with many spectral bands. Hence, it is essential to develop a more efficient neural network architecture to improve performance in the HSI classification task. Deep learning models are contemporary techniques for pixel-based hyperspectral image (HSI) classification. Deep feature extraction from both spatial and spectral channels has led to high classification accuracy. Meanwhile, the effectiveness of these spatial-spectral methods relies on the spatial dimension of every patch, and there is no feasible method to determine the best spatial dimension to take into consideration. It makes better sense to retrieve spatial properties through examination at different neighborhood scales in spatial dimensions. In this context, this paper presents a multi-scale hybrid spectral convolutional neural network (MS-HybSN) model that uses three distinct multi-scale spectral-spatial patches to pull out properties in spectral and spatial domains. The presented deep learning framework uses three patches of different sizes in spatial dimension to find these possible features. The process of Hybrid convolution operation (3D-2D) is done on each selected patch and is repeated throughout the image. To assess the effectiveness of the presented model, three benchmark datasets that are openly accessible (Pavia University, Indian Pines, and Salinas) and new Indian datasets (Ahmedabad-1 and Ahmedabad-2) are being used in experimental studies. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification performance
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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