Multispectral image change detection is an important application in the field of remote sensing. Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. The multi-scale adaptive kernel network (MSAK-Net) extends the encoding path of the U-Net, and designs a weight-sharing bilateral encoding path, which simultaneously extracts independent features of bi-temporal multispectral images without introducing additional parameters. A selective convolution kernel block (SCKB) that can adaptively assign weights is designed and embedded in the encoding path of MSAK-Net to extract multi-scale features in images. MSAK-Net retains the skip connections in the U-Net, and embeds an upsampling module (UM) based on the attention mechanism in the decoding path, which can give the feature map a better expression of change information in both the channel dimension and the spatial dimension. Finally, the multimodal conditional random field (MCRF) is used to smooth the detection results of the MSAK-Net. Experimental results on two public multispectral datasets indicate the effectiveness and robustness of the proposed method when compared with other state-of-the-art methods.

目的多光谱遥感图像林地变化检测是生态监测领域的重要研究方向之一。针对双时相图像中成像条件差异引起的伪变化，以及复杂地物变化在尺度与形态上的多样性问题，设计了一种频域增强分层交互网络，以提升复杂场景下林地变化检测的准确性与鲁棒性。方法首先，构建频域特征增强模块，在频域对双时相特征进行增强，以抑制由成像条件不一致引起的风格差异等伪变化干扰。其次，设计跨时相相关引导交互模块，通过对双时相特征的充分交互与相关性引导，增强网络对局部细微变化区域的识别能力。最后，通过空-谱耦合交互模块，对不同网络深度的变化特征进行整合，融合浅层细粒度结构信息与深层语义信息，实现对多尺度、多形态林地变化对象的联合感知。结果实验在3个林地变化检测数据集上与Spectral-Former、CSANet（cross-temporal interaction symmetric attention network）、TriTF（triplet Transformer framework）、DIEFEN（differential information-enhanced feature exchange network）、FrFTML（fractional Fourier transform domain decoupling-based adaptive weighted metric learning network）和AIWSEN（Adaptive information weighting and synchronized enhancement network）等先进方法进行了对比分析。在R1数据集中，所提出方法的Kappa系数达到0.8102，相比性能次优方法提升1.09%；在R2数据集中，Kappa系数提升2.62%；在R3数据集中，Kappa系数提升1.74%。同时，在三个数据集上的消融实验进一步验证了所提模块的有效性。结论本文所提方法能有效缓解双时相风格差异引起的伪变化干扰，并提升对细微变化与多尺度变化对象的检测能力，从而获得更为准确、稳定的林地变化检测结果。