Update README.md

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README.md

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----
-license: mit
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: hsi
-    dtype: string
-  - name: rgb
-    dtype: string
-  - name: segmentation
-    dtype: string
-  - name: spectra
-    dtype:
-      array2_d:
-        shape:
-        - 1
-        - 305
-        dtype: float32
-  splits:
-  - name: train
-    num_bytes: 717630
-    num_examples: 504
-  download_size: 521529
-  dataset_size: 717630
-task_categories:
-- image-classification
-- image-segmentation
-language:
-- en
-tags:
-- Hyperspectral
-- Robotics
-- Robot
-- Autonomous
-- Multispectral
-pretty_name: 'Hyper-Drive: Hyperspectral Driving Dataset'
-size_categories:
-- n<1K
----
+---
+license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: hsi
+    dtype: string
+  - name: rgb
+    dtype: string
+  - name: segmentation
+    dtype: string
+  - name: spectra
+    dtype:
+      array2_d:
+        shape:
+        - 1
+        - 305
+        dtype: float32
+  splits:
+  - name: train
+    num_bytes: 717630
+    num_examples: 504
+  download_size: 521529
+  dataset_size: 717630
+task_categories:
+- image-classification
+- image-segmentation
+language:
+- en
+tags:
+- Hyperspectral
+- Robotics
+- Robot
+- Autonomous
+- Multispectral
+pretty_name: 'Hyper-Drive: Hyperspectral Driving Dataset'
+size_categories:
+- n<1K
+---
+
+![](./hyper_drive.jpg)
+
 Towards automated analysis of large environments, hyperspectral sensors must be adapted into a format where they can be operated from mobile robots. In this dataset, we highlight hyperspectral datacubes collected from the [Hyper-Drive ](https://river-lab.github.io/hyper_drive_data/) imaging system. Our system collects and registers datacubes spanning the visible to shortwave infrared (660-1700 nm) in 33 wavelength channels. The system also simultaneously captures the ambient solar spectrum reflected off a white reference tile. The dataset consists of 500 labeled datacubes from on-road and off-road terrain compliant with the [ATLAS](http://gvsets.ndia-mich.org/documents/AAIR/2022/ATLAS,%20an%20All-Terrain%20Labelset%20for%20Autonomous%20Systems.pdf).
 
 This work first appeared at [WHISPERS](https://www.ieee-whispers.com/) 2023 In Athens, Greece and is a product of the  [Robotics and Intelligent Vehicles Research Laboratory (RIVeR)](https://www.robot.neu.edu/) at [Northeastern University](https://www.northeastern.edu/).