Land Classification Using Remotely Sensed Data: Going Multilabel

Karalas, Konstantinos; Tsagkatakis, Grigorios; Zervakis, M.; Tsakalides, Panagiotis

doi:10.1109/tgrs.2016.2520203

Cited by 42 publications

(24 citation statements)

References 48 publications

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“…Land Cover is a multi-label remote sensing image dataset collected by Karalas et al [11]. This dataset combines real satellite data from the Moderate Resolution Imaging Spectroradiometer instrument and high spatial resolution ground data from the CORINE Land Cover (CLC) project supported by European Environment Agency.…”

Section: Resultsmentioning

confidence: 99%

“…This dataset combines real satellite data from the Moderate Resolution Imaging Spectroradiometer instrument and high spatial resolution ground data from the CORINE Land Cover (CLC) project supported by European Environment Agency. We use the same features and labels as suggested in [11] for image annotation; each image is represented with 57 features with respect to 20 distinct labels, as depicted in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…In the end, MSC predicts the labels of the query image via linear combination of label vectors of labeled images weighted by the coefficients. However, MSC, like the solutions in [1,[9][10][11], is a supervised approach that asks for sufficient labeled images for sparse representation and dimensionality reduction. Nevertheless, it is very time consuming and expensive to collect sufficient labeled images.…”

Section: Related Workmentioning

confidence: 99%

“…Given that, it is necessary to take into account the multi labels' characteristics of remote sensing images and annotate a set of relevant labels to these images, instead of a single label. Recently, multi-label classification [7,8] that studies the problem where an instance annotated with a set of labels has been applied to annotate multi-label remote sensing images [1,[9][10][11] and has shown appealing performance in remote sensing applications. The multi-label classification framework has been applied to numerous real-world applications [12].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

et al. 2017

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Abstract:Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

et al. 2017

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“…Unlike one-hot vectors, a binary sequence is allowed to contain more than one 'hot' value for indicating the joint existence of multiple candidate classes in one image. Besides, several researches [35] formulate multi-label classification into several single-label classification tasks, and thus, train a set of binary classifiers for each class. Notably, one common assumption of these studies is that classes are independent of each other, and classifiers predict the existence of each category independently.…”

Section: An Observationmentioning

confidence: 99%

Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification

Hua

Mou

Zhu

2019

ISPRS Journal of Photogrammetry and Remote Sensing

135

103

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Aerial image classification is of great significance in the remote sensing community, and many researches have been conducted over the past few years. Among these studies, most of them focus on categorizing an image into one semantic label, while in the real world, an aerial image is often associated with multiple labels, e.g., multiple object-level labels in our case. Besides, a comprehensive picture of present objects in a given high-resolution aerial image can provide a more in-depth understanding of the studied region. For these reasons, aerial image multi-label classification has been attracting increasing attention. However, one common limitation shared by existing methods in the community is that the co-occurrence relationship of various classes, so-called class dependency, is underexplored and leads to an inconsiderate decision. In this paper, we propose a novel end-to-end network, namely class-wise attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM), for this task. The proposed network consists of three indispensable components: (1) a feature extraction module, (2) a class attention learning layer, and (3) a bidirectional LSTM-based sub-network. Particularly, the feature extraction module is designed for extracting fine-grained semantic feature maps, while the class attention learning layer aims at capturing discriminative class-specific features. As the most important part, the bidirectional LSTM-based sub-network models the underlying class dependency in both directions and produce structured multiple object labels. Experimental results on UCM multi-label dataset and DFC15 multi-label dataset validate the effectiveness of our model quantitatively and qualitatively.

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Multilabel classification of remote sensed satellite imagery

Kumar

Abhishek

Singh

et al. 2020

Trans Emerging Tel Tech

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Multilabel scene classification has emerged as a critical research area in the domain of remote sensing. Contemporary classification models primarily emphasis on a single object or multiobject scene classification of satellite remote sensed images. These classification models rely on feature engineering from images, deep learning, or transfer learning. Comparatively, multilabel scene classification of very high resolution (V.H.R.) images is a fairly unexplored domain of research. Models trained for single label scene classification are unsuitable for the application of recognizing multiple objects in a single remotely sensed V.H.R. satellite image. To overcome this research gap, the current inquiry proposes to fine‐tune the state of the art convolutional neural network (C.N.N.) architectures for multilabel scene classification. The proposed approach pre trains C.N.N on the ImageNet dataset and further fine‐tunes them to the task of detecting multiple objects in V.H.R. images. To understand the efficacy of this approach, the final models are applied on a V.H.R. dataset: the U.C.M.E.R.C.E.D. image dataset containing 21 different terrestrial land use categories with a submeter resolution. The performance of the final models is compared with graph convolutional network‐based model by Khan et al. From the results on performance metrics, it was observed that proposed models achieve comparable results in significantly fewer epochs.

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Land Classification Using Remotely Sensed Data: Going Multilabel

Cited by 42 publications

References 48 publications

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification

Multilabel classification of remote sensed satellite imagery

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