RETRACTED ARTICLE: Wiener filter based deep convolutional network approach for classification of satellite images

Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

“…Some papers intend multiple targets. The distribution is shown in Figure 2, which shows fewer studies on urban land cover [96,100,134,[136][137][138][139]. With the availability of VHR images, smaller urban features have been segmented in the majority of papers.…”

“…Some used a stack of multiple channels of images [95,101,144,160]. Some used filters like unsharp mask filter, median filter, linear contrast filter [106] and Wiener filter [139]. Most papers that used datasets like ISPRS or IEEE did not perform pre-processing on images, because the images were already in a ready-to-use condition, and consisted of annotated labels too.…”

“…Du et al (2020) [124] obtained two initial probabilistic labeling predictions using a DeepLabv3+ network on spectral image and an RF classifier on hand-crafted features, which they integrated by Dempster-Shafer (D-S) evidence theory to be fed into an object-constrained higher-order CRF framework to estimate the final semantic labeling results with the consideration of the spatial contextual information. Some other post-processing methods also include denoise and smoothing [100] and Otsu's thresholding [139]. Li…”