143 research outputs found
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing
imagery (HSI) is aimed at learning a binary classifier from positive and
unlabeled data, which has broad prospects in various earth vision applications.
However, when PU learning meets limited labeled HSI, the unlabeled data may
dominate the optimization process, which makes the neural networks overfit the
unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU
learning, which reduces the weight of the gradient of the unlabeled data by
Taylor series expansion to enable the network to find a balance between
overfitting and underfitting. In addition, the self-calibrated optimization
strategy is designed to stabilize the training process. Experiments on 7
benchmark datasets (21 tasks in total) validate the effectiveness of the
proposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.Comment: Accepted to ICCV 202
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery
For high spatial resolution (HSR) remote sensing images, bitemporal
supervised learning always dominates change detection using many pairwise
labeled bitemporal images. However, it is very expensive and time-consuming to
pairwise label large-scale bitemporal HSR remote sensing images. In this paper,
we propose single-temporal supervised learning (STAR) for change detection from
a new perspective of exploiting object changes in unpaired images as
supervisory signals. STAR enables us to train a high-accuracy change detector
only using \textbf{unpaired} labeled images and generalize to real-world
bitemporal images. To evaluate the effectiveness of STAR, we design a simple
yet effective change detector called ChangeStar, which can reuse any deep
semantic segmentation architecture by the ChangeMixin module. The comprehensive
experimental results show that ChangeStar outperforms the baseline with a large
margin under single-temporal supervision and achieves superior performance
under bitemporal supervision. Code is available at
https://github.com/Z-Zheng/ChangeStarComment: ICCV 202
A New Fuzzy Clustering Algorithm Based on Clonal Selection for Land Cover Classification
A new fuzzy clustering algorithm based on clonal selection theory from artificial immune systems (AIS), namely, FCSA, is proposed to obtain the optimal clustering result of land cover classification without a priori assumptions on the number of clusters. FCSA can adaptively find the optimal number of clusters and is designed as a two-layer system: the classification layer and the optimization layer. The classification layer of FCSA, inspired by clonal selection theory, generates the optimal classification result with a fixed cluster number by utilizing the clone, mutation, and selection of immune operators. The optimization layer of FCSA evaluates the optimal solutions according to performance measures for cluster validity and then adjusts the cluster number to output the final optimal cluster number. Two experiments with different types of image evince that FCSA not only finds the optimal number of clusters, but also consistently outperforms the traditional clustering algorithms, such as K-means and Fuzzy C-means. Hence, FCSA provides an effective option for performing the task of land cover classification
Learning One-Class Hyperspectral Classifier from Positive and Unlabeled Data for Low Proportion Target
Hyperspectral imagery (HSI) one-class classification is aimed at identifying
a single target class from the HSI by using only positive labels, which can
significantly reduce the requirements for annotation. However, HSI one-class
classification is far more challenging than HSI multi-class classification, due
the lack of negative labels and the low target proportion, which are issues
that have rarely been considered in the previous HSI classification studies. In
this paper, a weakly supervised HSI one-class classifier, namely HOneCls is
proposed to solve the problem of under-fitting of the positive class occurs in
the HSI data with low target proportion, where a risk estimator -- the
One-Class Risk Estimator -- is particularly introduced to make the full
convolutional neural network (FCN) with the ability of one class
classification. The experimental results obtained on challenging hyperspectral
classification datasets, which includes 20 kinds of ground objects with very
similar spectra, demonstrate the efficiency and feasibility of the proposed
One-Class Risk Estimator. Compared with the state-of-the-art one-class
classifiers, the F1-score is improved significantly in the HSI data with low
target proportion
Directive local color transfer based on dynamic look-up table
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