Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.

High-resolution (HR) satellite images, due to the technical constraints on spectral and spatial resolutions, usually contain only several broad spectral bands but with a very high spatial resolution. This provides rich spatial details of the objects on the Earth surface, while their spectral discrimination is relatively low. Recently, the increase of the satellite revisit times made it possible to acquire more frequent data coverage for finer classification. In this paper, we proposed a novel multitemporal deep fusion network (MDFN) for short-term multitemporal HR images classification. Specifically, a two-branch structure of MDFN is designed, which includes a long short-term memory (LSTM) and a convolutional neural network (CNN). The LSTM branch is mainly used to learn the joint expression of different temporal-spectral features. For the CNN branch, the 3D convolution is firstly applied along the temporal and spectral dimensions to jointly learn the temporal-spatial and spectral-spatial information, respectively, and then the 2D convolution is performed along the spatial dimension to further extract the spatial context information. Finally, features generated from the two different branches are fused to obtain the discriminative high-level semantic information for classification. Experimental results carried on two real multitemporal HR remote sensing data sets demonstrate that the proposed MDFN provides better classification performance over the state-of-the-art methods, and it also shows the potentiality to use short-term multitemporal HR images for more accurate Land Use/Land Cover (LULC) mapping.

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