Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Wang, Qi; He, Xiange; Li, Xuelong

doi:10.1109/tgrs.2018.2862899

Cited by 202 publications

(82 citation statements)

References 43 publications

(53 reference statements)

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“…Low-level features are often used for image analysis [35]. Employing the extracted low-level features of objects for object detection has been a very common method used by many scholars.…”

Section: Geospatial Object Detectionmentioning

confidence: 99%

A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

Guo

et al. 2019

Remote Sensing

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Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In this paper, we propose a novel multi-model decision fusion framework which takes contextual information and multi-region features into account for addressing those problems. First, a contextual information fusion sub-network is designed to fuse both local contextual features and object-object relationship contextual features so as to deal with the problem of the diversity and complexity of geospatial object appearance. Second, a part-based multi-region fusion sub-network is constructed to merge multiple parts of an object for obtaining more spatial structure information about the object, which helps to handle the problem of the insufficient understanding of geospatial object spatial structure information. Finally, a decision fusion is made on all sub-networks to improve the stability and robustness of the model and achieve better detection performance. The experimental results on a publicly available ten class data set show that the proposed method is effective for geospatial object detection.

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“…Low-level features are often used for image analysis [35]. Employing the extracted low-level features of objects for object detection has been a very common method used by many scholars.…”

Section: Geospatial Object Detectionmentioning

confidence: 99%

A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

Guo

et al. 2019

Remote Sensing

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“…The complex spatial and spectral distributions make it difficult to classify the objects in HSI, a good methodology to incorporate the spatial feature and spectral feature for HSI classification is the most concerned issue in this field. In Wang's locality and structure regularized low rank representation (LSLRR) model [14], a new distance metric is introduced to combine both spatial and spectral features. However, the spectral bands in HSI are much more redundant than its spatial information, thus many traditional methods extract the most discriminative spatial features or bands and use them to train the classifiers.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network With Mapping Layers for Hyperspectral Image Classification

Pan

Yang

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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In this paper, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low dimensional subspace by multilinear algebra. We use our mapping layers to reduce the spectral and spatial redundancy and maintain most energy of the input. The feature extracted by our mapping layers can also reduce the number of following convolutional layers for feature extraction. Our MCNN architecture avoids the declining accuracy with increasing layers phenomenon of deep learning models for HSI classification and also saves the training time for its effective mapping layers. Furthermore, we impose the 3-D convolutional kernel on convolutional layer to extract the spectral-spatial features for HSI. We tested our MCNN on three datasets of Indian Pines, University of Pavia and Salinas, and we achieved the classification accuracy of 98.3%, 99.5% and 99.3%, respectively. Experimental results demonstrate that the proposed MCNN can significantly improve the classification accuracy and save much time consumption.

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“…Although the basic RSIR system exhibited in Figure 1 is simple, the implementation is not as easy as as imaginary. For one RS scene, there are many specific characteristics that increase the difficulty of RSIR [8][9][10][11]. For example, the objects within one RS scene are diverse in type and huge in volume; the scales of the same objects may be different; and various thematic classes may be contained.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval

et al. 2018

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Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR.

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Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Cited by 202 publications

References 43 publications

A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images

A Convolutional Neural Network With Mapping Layers for Hyperspectral Image Classification

Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval

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