Abstract
Benggang is a typical erosional landform in southern and southeastern China. Since benggang poses significant risks to local ecological environments and economic infrastructure, it is vital to accurately detect benggang-eroded areas. Relying only on remote sensing imagery for benggang detection cannot produce satisfactory results. In this study, we propose integrating high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data for efficient and automatic benggang discovery. The fusion of complementary rich information hidden in both DOM and DSM data is realized by a two-stream convolutional neural network (CNN), which integrates aggregated terrain and activation image features that are both extracted by supervised deep learning. We aggregate local low-level geomorphic features via a supervised diffusion-convolutional embedding branch for expressive representations of benggang terrain variations. Activation image features are obtained from an image-oriented convolutional neural network branch. The two sources of information (DOM and DSM) are fused via a gated neural network, which learns the most discriminative features for the detection of benggang. The evaluation of a challenging benggang dataset demonstrates that our method exceeds several baselines, even with limited training examples. The results show that the fusion of DOM and DSM data is beneficial for benggang detection via supervised convolutional and deep fusion networks.
Highlights
Introduction published maps and institutional affilBenggang is a Chinese word for a typical gully erosional landform [1]
We propose using a two-stream convolutional neural network (CNN) framework to integrate aggregated terrain and image features for benggang discovery using high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data; We develop a supervised, diffusive convolutional encoding scheme that aggregates local geomorphic features, yielding expressive terrain representations for benggang; The developed deep fusion model is evaluated with a challenging benggang dataset
The empirical improvements over VGG-DOM and diffusive convolutional neural network (DCNN)-DSM could be attributed to the fusion of both DOM and DSM information
Summary
Benggang is a Chinese word for a typical gully erosional landform [1]. Benggang means “slope collapse” or “collapsing gully” in English. Benggang can be found in hilly areas covered by weathered granite crusts in southern and southeastern. The development of benggang is caused by collective impacts of gravity and runoff water, involving complex processes of sediment collapsing and transport [2]. Apart from natural factors, anthropogenic activities that destroy vegetation cover contribute to the development of benggang [3]. Continuous benggang erosions at gully heads result in chair-like forms with fragmented landscapes. Many studies have investigated the geographical distributions, development mechanisms, and erosion patterns of benggang landscapes [2,3,4,5]
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