scholarly journals Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

2021 ◽  
Vol 13 (1) ◽  
pp. 158
Author(s):  
Qiang Chen ◽  
Qianhao Cheng ◽  
Jinfei Wang ◽  
Mingyi Du ◽  
Lei Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Bare Soil ◽  
Classification Model ◽  
Feature Analysis ◽  
Feature Subset ◽  
Analysis Method ◽  
Remote Sensing Images ◽  
Construction Waste ◽  
Hierarchical Segmentation ◽  
Urban Construction

With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation.

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

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.

scholarly journals Weakly Supervised Change Detection Based on Edge Mapping and SDAE Network in High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (23) ◽  
pp. 3907
Author(s):  
Ning Lu ◽  
Can Chen ◽  
Wenbo Shi ◽  
Junwei Zhang ◽  
Jianfeng Ma
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
High Efficiency ◽  
Deep Structure ◽  
Ground Truth ◽  
Classification Model ◽  
Remote Sensing Images ◽  
Pixel Pair ◽  
Weakly Supervised

Change detection for high-resolution remote sensing images is more and more widespread in the application of monitoring the Earth’s surface. However, on the one hand, the ground truth could facilitate the distinction between changed and unchanged areas, but it is hard to acquire them. On the other hand, due to the complexity of remote sensing images, it is difficult to extract features of difference, let alone the construction of the classification model that performs change detection based on the features of difference in each pixel pair. Aiming at these challenges, this paper proposes a weakly supervised change detection method based on edge mapping and Stacked Denoising Auto-Encoders (SDAE) network called EM-SDAE. We analyze the difference in edge maps of bi-temporal remote sensing images to acquire part of the ground truth at a relatively low cost. Moreover, we design a neural network based on SDAE with a deep structure, which extracts the features of difference so as to efficiently classify changed and unchanged regions after being trained with the ground truth. In our experiments, three real sets of high-resolution remote sensing images are employed to validate the high efficiency of our proposed method. The results show that accuracy can even reach up to 91.18% with our method. In particular, compared with the state-of-the-art work (e.g., IR-MAD, PCA-k-means, CaffeNet, USFA, and DSFA), it improves the Kappa coefficient by 27.19% on average.

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