Land-Use Classification via Extreme Learning Classifier Based on Deep Convolutional Features

Weng, Qian; Mao, Zhengyuan; Jia-jun, Lin; Guo, Wenzhong

doi:10.1109/lgrs.2017.2672643

Cited by 114 publications

(53 citation statements)

References 21 publications

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“…In [44,46,[51][52][53]71], deep features from pre-trained CNNs were reprocessed to achieved excellent performance. In [73], a deep-learning-based classification method was presented to improve classification performance by combining pre-trained CNNs and extreme learning machine (ELM). In [56,72,74], deep features and hand-crafted features were combined to get a discriminative scene presentation.…”

Section: Comparisons With the Most Recent Methodsmentioning

confidence: 99%

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Hong

2019

Remote Sensing

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High spatial resolution remote sensing (HSRRS) images contain complex geometrical structures and spatial patterns, and thus HSRRS scene classification has become a significant challenge in the remote sensing community. In recent years, convolutional neural network (CNN)-based methods have attracted tremendous attention and obtained excellent performance in scene classification. However, traditional CNN-based methods focus on processing original red-green-blue (RGB) image-based features or CNN-based single-layer features to achieve the scene representation, and ignore that texture images or each layer of CNNs contain discriminating information. To address the above-mentioned drawbacks, a CaffeNet-based method termed CTFCNN is proposed to effectively explore the discriminating ability of a pre-trained CNN in this paper. At first, the pretrained CNN model is employed as a feature extractor to obtain convolutional features from multiple layers, fully connected (FC) features, and local binary pattern (LBP)-based FC features. Then, a new improved bag-of-view-word (iBoVW) coding method is developed to represent the discriminating information from each convolutional layer. Finally, weighted concatenation is employed to combine different features for classification. Experiments on the UC-Merced dataset and Aerial Image Dataset (AID) demonstrate that the proposed CTFCNN method performs significantly better than some state-of-the-art methods, and the overall accuracy can reach 98.44% and 94.91%, respectively. This indicates that the proposed framework can provide a discriminating description for HSRRS images. 2 of 23 color features, spectral features, and multi-feature fusion [23,24]. However, these hand-crafted features are limited in describing complex scenes of HSRRS, which will affect the classification performance. Compared with the low-level methods, the mid-level methods aim to obtain a global representation of a scene by encoding local descriptors, e.g., scale-invariant feature transform, histogram of oriented gradient and color histogram. The bag-of-view-word (BoVW) model is one of the most popular feature encoding approaches [25]. Due to its simplicity and efficiency, the BoVW model is widely applied for mid-level scene description [26][27][28][29]. However, the quantization error of the BoVW method is large, and some important information may be lost. Therefore, many feature coding methods were developed to reduce the reconstruction error, including improved Fisher kernel (IFK) [30], vectors of locally aggregated descriptors (VLAD) [31], spatial pyramid matching kernel (SPM) [32], locality-constrained linear coding (LLC) [33], latent semantic analysis (LSA), probabilistic latent semantic analysis (pLSA) [34,35], and latent dirichlet allocation (LDA) [35]. However, both low-level and mid-level methods are mainly based on hand-crafted features, which are difficult to effectively describe in HSRRS scene images with complex land-cover/land-use (LULC) situations.In recent years, deep-learning-based methods have made a...

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Section: Comparisons With the Most Recent Methodsmentioning

confidence: 99%

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Hong

2019

Remote Sensing

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“…This avoids having to retrain the networks which can take from hours to days even on powerful GPUs. Hence, many work adopt deep learning to advance the state-of-the-art in land use classification [26], [27], [28], [29], [30], [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

Zhu

Deng

Newsam

2019

IEEE Trans. Multimedia

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We perform fine-grained land use mapping at the city scale using ground-level images. Mapping land use is considerably more difficult than mapping land cover and is generally not possible using overhead imagery as it requires close-up views and seeing inside buildings. We postulate that the growing collections of georeferenced, ground-level images suggest an alternate approach to this geographic knowledge discovery problem. We develop a general framework that uses Flickr images to map 45 different land-use classes for the City of San Francisco. Individual images are classified using a novel convolutional neural network containing two streams, one for recognizing objects and another for recognizing scenes. This network is trained in an end-to-end manner directly on the labeled training images. We propose several strategies to overcome the noisiness of our user-generated data including search-based training set augmentation and online adaptive training. We derive a ground truth map of San Francisco in order to evaluate our method. We demonstrate the effectiveness of our approach through geo-visualization and quantitative analysis. Our framework achieves over 29% recall at the individual land parcel level which represents a strong baseline for the challenging 45-way land use classification problem especially given the noisiness of the image data.

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“…In [26], it also shows that the ELM can outperform SVM. In [27], the authors have confirmed that the CNN-ELM outperforms CNN-SVM in the area of high-resolution aerial scene classification. Therefore, ELM with CNN-learned features can perform excellently.…”

Section: Introductionmentioning

confidence: 97%

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

Fu-xian

2018

Computational Intelligence and Neuroscience

110

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One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

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Land-Use Classification via Extreme Learning Classifier Based on Deep Convolutional Features

Cited by 114 publications

References 21 publications

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

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