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- W2552603604 abstract "In this paper, we propose a novel approach for detecting multiple changes from two multi-temporal images. Despite the development of the change vector analysis (CVA) framework and its improved version the compressed CVA (C2VA) framework, it is found that they are limited when tackling the multi-change detection task for the images with one channel. Also, the intensity itself is fragile due to the existing noise, which especially influences the detection of subtle changes. Therefore, the stacked denosing autoencoder (SDAE) which serves as a fine tool for feature extraction is employed to generate a multi-dimensional feature representations. In this way, the C2VA framework can be applied to the inner robust features so that a satisfactory performance can be guaranteed. Experimental results from two datasets show its high accuracy and moderate time complexity, which demonstrates the effectiveness of the proposed SDAE-C2VA approach." @default.
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- W2552603604 date "2016-07-01" @default.
- W2552603604 modified "2023-09-25" @default.
- W2552603604 title "Detecting multiple changes from multi-temporal images by using stacked denosing autoencoder based change vector analysis" @default.
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- W2552603604 doi "https://doi.org/10.1109/ijcnn.2016.7727343" @default.
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