Multi-Scale Feature Based Land Cover Change Detection in Mountainous Terrain Using Multi-Temporal and Multi-Sensor Remote Sensing Images

Song, Fei; Yang, Zhuoqian; Gao, Xueyan; Dan, Tingting; Yang, Yang; Zhao, Wanjing; Yu, Rui

doi:10.1109/access.2018.2883254

Cited by 21 publications

(15 citation statements)

References 44 publications

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“…However, fuzzy clustering makes the model not restricted to the difference in registration, but to indicate the most possible change position. For example, Song [22] obtains the feature similarity matrix through the FCM cluster, based on the registration results of the L2-minimizing estimate-based energy optimization. It has been proved that the clustering method possesses the ability for self-development, giving consideration to both the accuracy and feasibility of heterogeneous images.…”

Section: Methods Of Feature Clusteringmentioning

confidence: 99%

“…Therefore, in addition to the early feature matching algorithm, such as wavelet transform [20] and optical flow [21], the current researches advocate taking deep networks to map key features or descriptors (i.e., scale-invariant features, contours, line intersections, corners, etc.) based on the preliminary results of geographic registration [22,23]. They seek correspondence and similarity between features, or perform stereo matching for three-dimensional (3D) modeling to solve problems about obstructions and multi-dimensional data [24,25].…”

Section: Change Detection Frameworkmentioning

confidence: 99%

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A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

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Section: Methods Of Feature Clusteringmentioning

confidence: 99%

Section: Change Detection Frameworkmentioning

confidence: 99%

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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“…Multi-temporal remote sensing image registration is a key preprocessing step for remote sensing image fusion [1,2], change detection [3,4], super-resolution reconstruction [5], and collaborative analysis [6]. In recent years, remote sensing image registration has mostly focused on solving the effects of radiation differences and geometric distortions by constructing robust feature descriptors [7,8], better screening of mismatched features [9,10], integrating multiple methods [11,12], and so on.…”

Section: Introductionmentioning

confidence: 99%

Coarse-to-Fine Image Registration for Multi-Temporal High Resolution Remote Sensing Based on a Low-Rank Constraint

Zhang

Luo

et al. 2022

Remote Sensing

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For multi-temporal high resolution remote sensing images, the image registration is important but difficult due to the high resolution and low-stability land-cover. Especially, the changing of land-cover, solar altitude angle, radiation intensity, and terrain fluctuation distortion in the overlapping areas can represent different image characteristics. These time-varying properties cause traditional registration methods with known reference information to fault. Therefore, in this paper we propose a comprehensive coarse-to-fine registration (CCFR) algorithm. First, we design a low-rank constraint-based batch reference extraction (LRC-BRE) method. Under the low-rank constraint, the stable features with highly spatial co-occurrence can be reconstructed via matrix decomposition, and are set as reference images to batch registration. Second, we improve the general feature registration with block feature matching and local linear transformation (BFM-LLT) operators including match outlier filtering (MOF) on regional mutual information and dual-weighted block fitting (DWBF). Finally, based on combining LRC-BRE and BFM-LLT, CCFR is integrated. Experimental results show that the proposed method has a good batch alignment effect, especially in the registration of large difference image pairs. The proposed CCFR achieves a significant performance improvement over many state-of-the-art registration algorithms.

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“…In the past decade, a series of high-resolution satellites have launched a new era of satellite remote sensing. The acquisition of high-resolution remote sensing images is more convenient, with high spatial resolution and rich detailed information of ground features, which has important and far-reaching significance for the monitoring of land-use change [6][7][8], building change [9,10], vegetation ecological monitoring [11][12][13], disaster monitoring and evaluation [14,15], and coastline change [16]. A series of classical remote sensing change detection methods developed in the past few decades have also been applied in the change detection of high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

et al. 2021

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Multi-Scale Feature Based Land Cover Change Detection in Mountainous Terrain Using Multi-Temporal and Multi-Sensor Remote Sensing Images

Cited by 21 publications

References 44 publications

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

Coarse-to-Fine Image Registration for Multi-Temporal High Resolution Remote Sensing Based on a Low-Rank Constraint

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

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