Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

Deep learning in remote sensing applications: A meta-analysis and review

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

in the field of remote sensing, ranging from pre- processing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

pdf/a-survey-deep-learning-for-hyperspectral-image-3stklr27s6.pdf

A survey: Deep learning for hyperspectral image classification with few labeled samples

Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.

DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing

Hyperspectral images (HSIs) denoising aims at eliminating the noise generated during the acquisition and transmission of HSIs. Since denoising is an ill-posed problem, utilizing proper knowledge of HSIs as regularization is essential for a good denoiser. Many HSI denoising methods have been proposed to leverage various prior knowledge, e.g., total variation, sparsity, and so on. Among those knowledge, a low-rank property has been shown to be effective for HSI denoising since it has the ability to deal with the missing values. However, most existing low-rank methods seldom consider mining the useful structures inside the low-rank matrix for a better denoising result. In addition, the rank number needs to be assigned manually. To address these problems, we propose an intracluster structured low-rank matrix analysis method for HSI denoising. First, we divide the original HSI into some clusters by taking advantages of both local similarity and nonlocal similarity structures, with which the resulted clusters are simpler and show more obvious low-rank property. Second, with singular value decomposition on the low-rank matrix in each cluster, the structured sparsity is modeled among the singular values to capture the structure of the low-rank matrix. Finally, an efficient optimization method is proposed to learn the structured sparsity adaptively from the data, as well as to inversely estimate the latent clean HSI from the noisy counterpart. The proposed method can not only obtain better denoising results compared with the-state-of-the-art methods but also automatically determine the rank number. Extensive experimental results demonstrate the effectiveness of the proposed method.

Intracluster Structured Low-Rank Matrix Analysis Method for Hyperspectral Denoising

When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc.) among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.

/pdf/when-low-rank-representation-based-hyperspectral-imagery-4ccug3e3l8.pdf

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

Recently, deep convolutional neural network (DCNN)-based methods have achieved much success in hyperspectral image (HSI) classification, when sufficient labeled samples are provided during training. However, due to the expensive cost of labeling in HSIs, only limited labeled samples can be given in practice, which often causes these methods to be overfitting. To address this problem, we present a new HSI classification method in this study, which is constructed in the following two steps. First, we establish a data mixture model to augment the labeled training set quadratically and train a DCNN-based classifier on it. Then, through randomly sampling the coefficient in the data mixture model, we obtain several independent classifiers and fuse them with a voting strategy to produce the final classification results. Since both data augmentation and classifier fusion are effective to deal with limited samples, the proposed method shows superior performance in the classification of HSIs, which can be demonstrated by the experimental results on two benchmark HSI data sets.

Hyperspectral Image Classification With Data Augmentation and Classifier Fusion

Hyperspectral image (HSI) classification is an important application of HSI analysis, which aims at assigning a class label to each pixel. However, considering that mixed pixels commonly exist in HSI, assigning a unique label to each pixel is imprecise. To better analysis the scene imaged in an HSI, we propose a multi-label hyperspectral image classification approach based on deep learning in this study. First, stacked denoising autoencoder (SDAE) method is used to extract deep features for each pixel without supervision, which can well represent the nonlinearity of the mixed pixels in a high dimensional feature space. Then, multi-label logistic regression method assigns each pixel multi labels. Experimental results on the synthetic data, real hyperspectral data and down-sampling hyperspectral data demonstrate the effectiveness of the proposed method.

A multi-label Hyperspectral image classification method with deep learning features

Deep neural networks have been proven to be a promising way for hyperspectral image (HSI) classification. Their success depends on a premise that source domain (i.e., training) and target domain (i.e., test) samples are identically distributed. However, due to various imaging environments, in practice obvious distribution discrepancy often exists between these two domains, which can dramatically reduce the capacity of the classifier trained in source domain generalizing to target domain. To mitigate this problem, we present a novel deep unsupervised domain adaptation framework for HSI classification, which can simultaneously align the distributions of two domains and learn a classifier in source domain. Firstly, we employ two auto-encoder networks to separately project the samples from two domains into two low-dimensional feature spaces. Then, a multi-level maximum mean discrepancy (MMD) loss is imposed on the feature space to reduce the distribution discrepancy between two domains. Given the resultant features, a classification subnet is further learned to classify the labeled samples in source domain. Since the classifier is trained based on the domain-invariant features, it can well generalize to the target domain. Experimental results on one benchmark cross-domain HSI datasets prove the superior performance of the proposed method.

Unsupervised deep domain adaptation for hyperspectral image classification

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Cong Wang

Author Tools

Chat about Author