Encoder-Decoder based CNN and Fully Connected CRFs for Remote Sensed Image Segmentation

With the advancement of remote-sensed imaging large volumes of very high resolution land cover images can now be obtained. Automation of object recognition in these 2D images, however, is still a key issue. High intra-class variance and low inter-class variance in Very High Resolution (VHR) images hamper the accuracy of prediction in object recognition tasks. Most successful techniques in various computer vision tasks recently are based on deep supervised learning. In this work, a deep Convolutional Neural Network (CNN) based on symmetric encoder-decoder architecture with skip connections is employed for the 2D semantic segmentation of most common land cover object classes - impervious surface, buildings, low vegetation, trees and cars. Atrous convolutions are employed to have large receptive field in the proposed CNN model. Further, the CNN outputs are post-processed using Fully Connected Conditional Random Field (FCRF) model to refine the CNN pixel label predictions. The proposed CNN-FCRF model achieves an overall accuracy of 90.5% on the ISPRS Vaihingen Dataset.

Mango Tree Net -- A fully convolutional network for semantic segmentation and individual crown detection of mango trees

This work presents a method for semantic segmentation of mango trees in high resolution aerial imagery, and, a novel method for individual crown detection of mango trees using segmentation output. Mango Tree Net, a fully convolutional neural network (FCN), is trained using supervised learning to perform semantic segmentation of mango trees in imagery acquired using an unmanned aerial vehicle (UAV). The proposed network is retrained to separate touching/overlapping tree crowns in segmentation output. Contour based connected object detection is performed on the segmentation output from retrained network. Bounding boxes are drawn on the original images using coordinates of connected objects to achieve individual crown detection. The training dataset consists of 8,824 image patches of size 240 x 240. The approach is tested for performance on segmentation and individual crown detection tasks using test datasets containing 36 and 4 images respectively. The performance is analyzed using standard metrics precision, recall, f1-score and accuracy. Results obtained demonstrate the robustness of the proposed methods despite variations in factors such as scale, occlusion, lighting conditions and surrounding vegetation.