Peatland classification provides valuable information for greenhouse gas inventory and biodiversity protection. In this paper, we proposed an encoder-decoder-based architecture for peatland classification that fuses two open-source satellite data, Sentinel-1 and Sentinel-2. We show the effect of fusion by comparing the multi-modal fusion architecture with unimodals which are trained only based on one input data source. We also investigate the influence of skip connections as the main component of the encoder-decoder to recover fine-grained details that are lost during the downsampling process. The experimental results are acquired on a study area in Finland which covers a variety minerotrophic aapa mire peatlands. The results demonstrate that multi-modal architecture consistently outperforms uni-modal architectures for peatland classification. In addition, the fusion architecture with one skip connection achieved a total accuracy of $57.44 \%$. This shows $8.51 \%$ accuracy improvement compared with the model without skip connections.

Recently, Convolutional Neural Network (CNN) has shown higher performance than other machine learning methods for land classification. In this paper, we propose a CNN fusion architecture for peatland site type classification by combining multisource and multiresolution data. The data is acquired by optical and radar satellite remote sensing, airborne laser scanning data and multi-source forest inventory GIS datasets. Based on our data, we are dealing with the high-dimensional class-imbalanced dataset for solving pixel-wise classification of peatlands. To reduce the data dimension and find an optimal subset of inputs, we first applied the sequential feature selection method. Then, we proposed a window-based pixel classification approach based on the selected inputs. This approach can extract the spatial information around each training sample in a defined window region and produce a pixel-wise classification map. Experiments are carried out for ecological classification of peatlands in Finland.

The goal of this paper is answer to this question: how much fusing LiDAR and color images can improve the performance of a convolutional neural network (CNN)-based detector? To this end, we trained state-of-the-art CNN-based detectors using different configurations of color images and their associated LiDAR data, in conjunction and independently. Moreover, we investigate the effect of sparse and dense LiDAR data on the detection accuracy. For this purpose, we estimate a dense depth image from spare LiDAR data using a recent self-supervised depth completion technique [1] that requires only sequences of color and sparse depth images, without the need for dense depth labels. Then, we compared two detectors when are trained on sparse or dense LiDAR data. The obtained results on the KITTI dataset show that fusing dense LiDAR and color images is an efficient solution for future object detectors.