1,897 research outputs found
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
Hyperspectral image (HSI) classification, which aims to assign an accurate
label for hyperspectral pixels, has drawn great interest in recent years.
Although low rank representation (LRR) has been used to classify HSI, its
ability to segment each class from the whole HSI data has not been exploited
fully yet. LRR has a good capacity to capture the underlying lowdimensional
subspaces embedded in original data. However, there are still two drawbacks for
LRR. First, LRR does not consider the local geometric structure within data,
which makes the local correlation among neighboring data easily ignored.
Second, the representation obtained by solving LRR is not discriminative enough
to separate different data. In this paper, a novel locality and structure
regularized low rank representation (LSLRR) model is proposed for HSI
classification. To overcome the above limitations, we present locality
constraint criterion (LCC) and structure preserving strategy (SPS) to improve
the classical LRR. Specifically, we introduce a new distance metric, which
combines both spatial and spectral features, to explore the local similarity of
pixels. Thus, the global and local structures of HSI data can be exploited
sufficiently. Besides, we propose a structure constraint to make the
representation have a near block-diagonal structure. This helps to determine
the final classification labels directly. Extensive experiments have been
conducted on three popular HSI datasets. And the experimental results
demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201
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
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
Spectral-spatial classification of hyperspectral images has been the subject
of many studies in recent years. In the presence of only very few labeled
pixels, this task becomes challenging. In this paper we address the following
two research questions: 1) Can a simple neural network with just a single
hidden layer achieve state of the art performance in the presence of few
labeled pixels? 2) How is the performance of hyperspectral image classification
methods affected when using disjoint train and test sets? We give a positive
answer to the first question by using three tricks within a very basic shallow
Convolutional Neural Network (CNN) architecture: a tailored loss function, and
smooth- and label-based data augmentation. The tailored loss function enforces
that neighborhood wavelengths have similar contributions to the features
generated during training. A new label-based technique here proposed favors
selection of pixels in smaller classes, which is beneficial in the presence of
very few labeled pixels and skewed class distributions. To address the second
question, we introduce a new sampling procedure to generate disjoint train and
test set. Then the train set is used to obtain the CNN model, which is then
applied to pixels in the test set to estimate their labels. We assess the
efficacy of the simple neural network method on five publicly available
hyperspectral images. On these images our method significantly outperforms
considered baselines. Notably, with just 1% of labeled pixels per class, on
these datasets our method achieves an accuracy that goes from 86.42%
(challenging dataset) to 99.52% (easy dataset). Furthermore we show that the
simple neural network method improves over other baselines in the new
challenging supervised setting. Our analysis substantiates the highly
beneficial effect of using the entire image (so train and test data) for
constructing a model.Comment: Remote Sensing 201
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