The Spectral-Spatial Joint Learning for Change Detection in Multispectral Imagery

Abstract

Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the spectral and spatial information into account. Moreover, the underlying information of fused features is not fully explored. To address the above-mentioned problems, a Spectral-Spatial Joint Learning Network (SSJLN) is proposed. SSJLN contains three parts: spectral-spatial joint representation, feature fusion, and discrimination learning. First, the spectral-spatial joint representation is extracted from the network similar to the Siamese CNN (S-CNN). Second, the above-extracted features are fused to represent the difference information that proves to be effective for the change detection task. Third, the discrimination learning is presented to explore the underlying information of obtained fused features to better represent the discrimination. Moreover, we present a new loss function that considers both the losses of the spectral-spatial joint representation procedure and the discrimination learning procedure. The effectiveness of our proposed SSJLN is verified on four real data sets. Extensive experimental results show that our proposed SSJLN can outperform the other state-of-the-art change detection methods.

From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques

“…Wang et al [40] presented an end-to-end 2D CNN framework for hyperspectral image change detection, where mixed-affinity matrices are formed from which GETNET extracts features for classification. Zhang et al [41] proposed a spectral-spatial joint learning network (SSJLN), using a network similar to the siamese CNN to extract spectral-spatial joint representations and fuse them to represent difference information, and using the discrimination learning to explore the underlying information. Ma et al [42] proposed a change detection method for heterogeneous images based on pixel-level mapping and a capsule network with a deep structure.…”

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

“…The following are some representative and recent papers [77][78][79][80][81] based on sparsity optimization. Chen and Wang [77] proposed a Spectrally-Spatially (SS) Regularized Low-Rank and Sparse Decomposition (LRSD) model.…”

“…A Spectral-Spatial Joint Learning Network (SSJLN) was proposed in [80]. The key advantage is that both spectral and spatial information is taken into account.…”

Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks

“…DL-based change detection methods have been applied to different targets such as urban [46][47][48][49], land use/land cover [50][51][52], and landslides [53], among others. Peng et al [54] proposed a subdivision of DL-based change detection methods that considered three units of analysis: (1) feature [55][56][57]; (2) patch [58][59][60][61]; and (3) image [62,63]. In the case of image-based DL change detection, the algorithms learn the segmentation of changes directly from bi-temporal image pairs, avoiding the negative effects caused when using pixel patches [54].…”