A Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors

Marinelli, Daniele; Bovolo, Francesca; Bruzzone, Lorenzo

doi:10.1109/tgrs.2019.2894339

Cited by 70 publications

(55 citation statements)

References 61 publications

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“…As a particular type of remote sensing images with high spectral resolution, hyperspectral image (HSI) contains plentiful information both in the spectral and spatial dimension [5]. HSI has been used in many fields including vegetation cover monitoring [6], atmospheric environmental research [7], and change area detection [8], among others. Supervised classification is an essential task of HSI, and is the common technology used in the above applications.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

et al. 2020

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In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

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Section: Introductionmentioning

confidence: 99%

Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

et al. 2020

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“…Recently, remote sensing image has been studied in more and more areas, including image registration [1][2][3], change detection [4,5], object detection [6] and so on. As is known to all, Hyperspectral Imaging (HSI) is a special type of remote sensing image which has abundant spectral and spatial information [7], and has been studied in many fields, including forest vegetation cover monitoring [8], classification of land-use [9,10], change area detection [11], anomaly detection [12] and environmental protection [13].…”

Section: Introductionmentioning

confidence: 99%

Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

et al. 2019

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Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.

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“…Li et al assumed that the distribution of changing and non-changing classes was Gaussian distribution, and proposed Kittler-Illingworth threshold selection criteria [39]. Marinelli et al assumed that the distribution of changing and non-changing classes was non-Gaussian distribution, and used the generalized Kittler-Illingworth minimum error threshold algorithm to detect changes in SAR images [40]. The threshold method can produce a good classification effect when changed and non-changed classes of the statistical distribution belong to different modes in the histogram of the difference map.…”

Section: Introductionmentioning

confidence: 99%

Change Detection Algorithm for Multi-Temporal Remote Sensing Images Based on Adaptive Parameter Estimation

Zhang

Menenti

2020

IEEE Access

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This paper proposes a multi-temporal image change detection algorithm based on adaptive parameter estimation, which is used to solve the problems of severe interference of coherent speckle noise and the retention of detailed information about changing regions in synthetic aperture radar remote sensing images. The change area in the initial differential image has local consistency and global prominence. By detecting the significant area to locate similar change areas, the coherent speckle noise outside the area can be eliminated. The use of hierarchical FCM clustering to automatically generate training samples can improve the reliability of training samples. In addition, in order to increase the distinction between the changed area and the non-changed area, a sparse automatic encoder is used to extract the changed features and generate a change detection map. Experiments using 4 sets of SAR images show that the algorithm can effectively reduce the effect of speckle noise on detection accuracy, the extraction of changing areas is more complete and meticulous, and the false detection rate is greatly reduced. Since the images in different time phases will be disturbed by weather, clouds, sea water, etc., the target segmentation algorithm can be used to extract the target of interest and highlight the changing area. Principal component analysis and k-means clustering method are used to reduce the influence of isolated pixels, and change information is extracted to obtain different images. The experiment uses four sets of image data of islands and reefs. The experiment proves that the algorithm can well eliminate external interference, improve the accuracy of change detection, and have a good detection effect on the area of islands and reefs. The adaptive parameter estimation plays a good role in the detection of changing areas, and the visual effect is better, which can improve the accuracy of the detection results. INDEX TERMS Adaptive parameters, multi-temporal, remote sensing images, change detection model, superficial superposition.

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A Novel Change Detection Method for Multitemporal Hyperspectral Images Based on Binary Hyperspectral Change Vectors

Cited by 70 publications

References 61 publications

Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

Change Detection Algorithm for Multi-Temporal Remote Sensing Images Based on Adaptive Parameter Estimation

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