Affiliation:
1. School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China
2. Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, China
3. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510000, China
Abstract
Since individual neural networks have limited deep expressiveness and effectiveness, many learning frameworks face difficulties in the availability and balance of sample selection. As a result, in change detection, it is difficult to upgrade the hit rate of a high-performance model on both positive and negative pixels. Therefore, supposing that the sacrificed components coincide perfectly with the important evaluation objectives, such as positives, it would lose more than gain. To address this issue, in this paper, we propose a multi-visual collaborative deep network (MV-CDN) served by three collaborative network members that consists of three subdivision approaches, the CDN with one collaborator (CDN-C), CDN with two collaborators (CDN-2C), and CDN with three collaborators (CDN-3C). The purpose of the collaborator is to re-evaluate the feature elements in the network transmission, and thus to translate the group-thinking into a more robust field of vision. We use three sets of public double-temporal hyperspectral images taken by the AVIRIS and HYPERION sensors to show the feasibility of the proposed schema. The comparison results have confirmed that our proposed schema outperforms the existing state-of-the-art algorithms on the three tested datasets, which demonstrates the broad adaptability and progressiveness of the proposal.
Funder
Research project of the Macao Polytechnic University
Science and Technology Development Fund of Macau SAR
Subject
General Earth and Planetary Sciences
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