Hierarchical Coding Vectors for Scene Level Land-Use Classification

Wu, Haiyuan; Liu, Baozhen; Su, Weihua; Zhang, Wenchang; Sun, Jinggong

doi:10.3390/rs8050436

Cited by 47 publications

(33 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whereas, SIFT descriptor and HOG feature are local features that are used for the representations of local structure [108] and shape information [109]. To represent an entire scene image, they are generally used as building blocks to construct global image features, such as the well-known bag-of-visual-words (BoVW) models [6,8,9,14,19,29,36,38,39,55,93,101,122,123] and HOG feature-based part models [22,23,27,103]. In addition, a number of improved feature encoding/pooling methods have also been proposed in the past few years, such as Fisher vector coding [10,14,84,86], spatial pyramid matching (SPM) [124], and probabilistic topic model (PTM) [11,40,42,43,92,123], etc.…”

Section: A Handcrafted Feature Based Methodsmentioning

confidence: 99%

“…Owing to its simplicity, k-means clustering method is widely used for unsupervised feature learning-based scene image classification. The most representative example is BoVW-based methods [8,9,14,19,29,86,87,89,91,122,123,132,140] where the visual dictionaries (codebooks) are generated by performing k-means clustering on the set of local features.…”

Section: B Unsupervised Feature Learning Based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing Image Scene Classification: Benchmark and State of the Art

2017

View full text Add to dashboard Cite

This paper reviews the recent progress of remote sensing image scene classification, proposes a large-scale benchmark dataset, and evaluates a number of state-of-the-art methods using the proposed dataset.Gong Cheng, Junwei Han, Senior Member, IEEE, and Xiaoqiang Lu, Senior Member, IEEE Abstract-Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.

show abstract

Section: A Handcrafted Feature Based Methodsmentioning

confidence: 99%

Section: B Unsupervised Feature Learning Based Methodsmentioning

confidence: 99%

Remote Sensing Image Scene Classification: Benchmark and State of the Art

2017

View full text Add to dashboard Cite

show abstract

“…It is worthy to mention that our approach is complementary to (Scenario II: Conv features) method[27] and combining the two approaches can be expected to provide further gain in the classification performance.On the RSSCN7 dataset, the deep learning based feature selection approach (DBN)[83] achieves a mean recognition rate of 77.0%. The hierarchical coding vectors based classification approach[90] achieves a classification result of 86.4%. The deep filter banks approach[93] provides a classification performance of 90.4%.…”

mentioning

confidence: 99%

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Anwer

Khan

Weijer

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

245

View full text Add to dashboard Cite

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.Recently, Convolutional Neural Networks (CNNs) have revolutionised computer vision, being the catalyst to significant performance gains in many vision applications, including texture recognition [26] and remote sensing scene classification [27,28]. CNNs and other "deep networks" are generally trained on large amounts of labeled training data (e.g. ImageNet [29]) with raw image pixels with a fixed size as input. Deep networks consists of several convolution and pooling operations followed by one or more fully connected (FC) layers. Several works [30,31] have shown that intermediate activations of the FC layers in a deep network, pre-trained on the ImageNet dataset, are general-purpose features applicable to visual recognition tasks. Deep features based approaches have shown to provide the best results in recent evaluations for texture recognition [4] and remote sensing scene classification [32].As mentioned above, the de facto practice is to train deep models on the ImageNet dataset using RGB values of the image patch as an input to the network. These pre-trained RGB deep networks are typically employed...

show abstract

“…However, it is computationally expensive to directly train effective DNNs for visual terrain classification. For a good trade-off between effectiveness and efficiency, the BOVW framework [17,24,25] is used to generate a compact semantic representation with low-level descriptors for visual terrain classification, obtaining good accuracy and nice robustness. This visual terrain classification algorithm has been successfully applied in the small quadruped robot Littledog as a necessary function module [24].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Optimum Pipeline for Visual Terrain Classification Using Improved Bag of Visual Words and Fusion Methods

Wu¹,

Baozhen²,

Su³

et al. 2017

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

We propose an optimum pipeline and develop the hybrid representation to produce an effective and efficient visual terrain classification system. The bag of visual words (BOVW) framework has emerged as a promising approach and effective paradigm for visual terrain classification. The method includes four main steps: (1) feature extraction, (2) codebook generation, (3) feature coding, and (4) pooling and normalization. Recent researches have primarily focused on feature extraction in the development of new handcrafted descriptors that are specific to the visual terrain. However, the effects of other steps on visual terrain classification are still unknown. At the same time, fusion methods are often used to boost classification performance by exploring the complementarity of diverse features. We provide a comprehensive study of all steps in the BOVW framework and different fusion methods for visual terrain classification. Then, multiple approaches in each step and their effects are explored on the visual terrain dataset. Finally, the feature preprocessing technique, improved BOVW framework, and fusion method are used to construct an optimum pipeline for visual terrain classification. The hybrid representation developed by the optimum pipeline performs effectively and rapidly for visual terrain classification in the terrain dataset, outperforming those current methods. Furthermore, it is robust to diverse noises and illumination alterations.

show abstract

Hierarchical Coding Vectors for Scene Level Land-Use Classification

Cited by 47 publications

References 30 publications

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Optimum Pipeline for Visual Terrain Classification Using Improved Bag of Visual Words and Fusion Methods

Contact Info

Product

Resources

About