An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

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Cloud masking of multitemporal remote sensing images

10.1117/12.2067193 ◽

2014 ◽

Cited By ~ 1

Author(s):

L. Gómez-Chova ◽

J. Amorós-López ◽

J. Muñoz-Marí ◽

G. Camps-Valls

Keyword(s):

Remote Sensing ◽

Remote Sensing Images ◽

Multitemporal Remote Sensing

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DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030125 ◽

2021 ◽

Vol 10 (3) ◽

pp. 125

Author(s):

Junqing Huang ◽

Liguo Weng ◽

Bingyu Chen ◽

Min Xia

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Spatial Information ◽

Feature Fusion ◽

Semantic Segmentation ◽

Dual Function ◽

Context Information ◽

Remote Sensing Images ◽

Feature Aggregation ◽

Image Context

Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.

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DO-Net: Dual-Output Network for Land Cover Classification From Optical Remote Sensing Images

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3114305 ◽

2021 ◽

pp. 1-5

Author(s):

Wenchao Kang ◽

Yuming Xiang ◽

Feng Wang ◽

Hongjian You

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Land Cover Classification ◽

Optical Remote Sensing ◽

Remote Sensing Images

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An evaluation approach for segmentation results of high-resolution remote sensing images based on the degree distribution of land cover networks

International Journal of Modern Physics B ◽

10.1142/s0217979218502831 ◽

2018 ◽

Vol 32 (25) ◽

pp. 1850283

Author(s):

Jing He ◽

Gang Liu ◽

Weile Li ◽

Chuan Tang ◽

Jiayan Lu

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Degree Distribution ◽

Heavy Tails ◽

Object Oriented ◽

Topological Properties ◽

Analysis Method ◽

Remote Sensing Images ◽

Spectral Difference ◽

Evaluation Approach

Identifying the degree distribution of land cover networks is helpful to find analytical methods for characterizing complex land cover, including segmentation techniques of remote sensing images of land cover. After segmentation, we can obtain the geographical objects and corresponding relationships. In order to evaluate the segmentation results, we introduce the concept of land cover network and present an analysis method based on statistics of its degree distribution. Considering the object-oriented segmentation and objects merge-based spectral difference segmentation, we construct the land cover networks for different segmentation scales and spatial resolutions under these two segmentation strategies, and study the degree distribution of each land cover network. Experimental results indicate that, for the object-oriented segmentation, the degree distributions of land cover networks follow approximately a Poisson distribution, regardless of the segmentation scales and spatial resolutions. For the objects-merge method based on spectral difference segmentation, degree distributions exhibit heavy tails. Compared with all the segmentation results, the pattern spots after objects-merge better retain the integrity of geographical features and the land cover network can reflect more accurately the topological properties of real land cover when the threshold of objects merge is suitable. This study shows that we can evaluate the reliability of segmentation results objectively by analyzing the degree distribution pattern of land cover networks.

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