Hyperspectral remote sensing image change detection based on tensor and deep learning

Huang, Feng; Yu, Ying; Feng, Tinghao

doi:10.1016/j.jvcir.2018.11.004

Cited by 66 publications

(34 citation statements)

References 15 publications

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“…An RBM network [45,[63][64][65] is an energy-based generative NN, i.e., the model reaches the ideal state when the energy is minimal. The RBM network was also used for remote sensing image process tasks [66,67]. It is composed of two layers with binary values (Figure 2), i.e., a visible layer with m nodes v = (v 1 , v 2 , ..., v i , ..., v m ) T ∈ {0, 1} m (i = 1, 2, ...m) and a hidden layer with n nodes h = (h 1 , h 2 , ...h j , ...h n ) T ∈ {0, 1} n (j = 1, 2, ..., n).These two layers are used to input training, validation, and test data and to identify an intrinsic expression of the data.…”

Section: Basic Principle Of Rbmmentioning

confidence: 99%

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Tang

Chen

et al. 2019

Remote Sensing

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Land cover classification (LCC) of complex landscapes is attractive to the remote sensing community but poses great challenges. In complex open pit mining and agricultural development landscapes (CMALs), the landscape-specific characteristics limit the accuracy of LCC. The combination of traditional feature engineering and machine learning algorithms (MLAs) is not sufficient for LCC in CMALs. Deep belief network (DBN) methods achieved success in some remote sensing applications because of their excellent unsupervised learning ability in feature extraction. The usability of DBN has not been investigated in terms of LCC of complex landscapes and integrating multimodal inputs. A novel multimodal and multi-model deep fusion strategy based on DBN was developed and tested for fine LCC (FLCC) of CMALs in a 109.4 km 2 area of Wuhan City, China. First, low-level and multimodal spectral–spatial and topographic features derived from ZiYuan-3 imagery were extracted and fused. The features were then input into a DBN for deep feature learning. The developed features were fed to random forest and support vector machine (SVM) algorithms for classification. Experiments were conducted that compared the deep features with the softmax function and low-level features with MLAs. Five groups of training, validation, and test sets were performed with some spatial auto-correlations. A spatially independent test set and generalized McNemar tests were also employed to assess the accuracy. The fused model of DBN-SVM achieved overall accuracies (OAs) of 94.74%± 0.35% and 81.14% in FLCC and LCC, respectively, which significantly outperformed almost all other models. From this model, only three of the twenty land covers achieved OAs below 90%. In general, the developed model can contribute to FLCC and LCC in CMALs, and more deep learning algorithm-based models should be investigated in future for the application of FLCC and LCC in complex landscapes.

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Section: Basic Principle Of Rbmmentioning

confidence: 99%

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Tang

Chen

et al. 2019

Remote Sensing

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“…Change detection methods for HSIs must address the problems of high dimensionality, mixed pixels, high computational cost, and a limited training dataset. Effective AI algorithms can be employed to solve these problems and have been proved to achieve satisfactory performance [36][37][38][39].…”

Section: Optical Rs Imagesmentioning

confidence: 99%

“…However, its units within the same layer are not connected to each other and each hidden layer serves as the visible layer for the next. As a feature extractor, it can be trained greedily, i.e., one layer at a time, and appears in many unsupervised change detection methods [23,37,157,183]. On the other hand, the deep Boltzmann machine (DBM), as a graph similar to DBN but undirected, can also achieve such a function [182].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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“…Finally, the third block is an abundance estimation step, that finds the abundance (α) of all the individual endmembers in each mixed pixel. The most important block (and the one we specifically address in this paper) is the endmember extraction one, which provides prior information of pure materials for target detection [4], abundance mapping [5], change detection [6], and object classification [7]. As a result, proper extraction of pure endmembers is very important in hyperspectral data exploitation [8].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Convex Set Optimization for Spatial–Spectral Endmember Extraction From Hyperspectral Images

Shah

Zaveri

Trivedi

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spectral unmixing is an important problem for remotely sensed hyperspectral data exploitation. Automatic spectral unmixing can be viewed as a three-stage problem, where the first stage is subspace identification, the next one is endmember extraction, and the final one is abundance estimation. In this sequence, endmember extraction is the most challenging problem. Many researchers have attempted to extract endmembers from hyperspectral images using spectral information only. However, it is well known that the inclusion of spatial information can improve the endmember extraction task. In this paper, we introduce a new endmember extraction algorithm that exploits both spectral and spatial information. A main innovation of the proposed algorithm is that spatial information is exploited using entropy, while spectral information is exploited using convex set optimization. In the literature, none of the spatial-spectral algorithms has used entropy as spatial information. The inclusion of this entropy-based spatial information improves the accuracy of the endmember extraction process. The results obtained by the proposed algorithm are compared (using a variety of metrics) with those obtained by other state-of-the-art methods, using both synthetic and real datasets. Our experimental results demonstrate that the proposed algorithm outperforms many available algorithms.

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Hyperspectral remote sensing image change detection based on tensor and deep learning

Cited by 66 publications

References 15 publications

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Entropy-Based Convex Set Optimization for Spatial–Spectral Endmember Extraction From Hyperspectral Images

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